From 6e81b5a7d2b703c7361ab287cabc868f2d4df252 Mon Sep 17 00:00:00 2001
From: Pavel Tomanek <shavel.tom@gmail.com>
Date: Fri, 19 Jul 2024 11:21:28 +0200
Subject: [PATCH] #357 synthcity tests working + testlog

---
 357_Synthcity/synthcity.eb  |   190 +-
 357_Synthcity/test_log2.txt | 40386 ++++++++++++++++++++++++++++++++++
 2 files changed, 40552 insertions(+), 24 deletions(-)
 create mode 100644 357_Synthcity/test_log2.txt

diff --git a/357_Synthcity/synthcity.eb b/357_Synthcity/synthcity.eb
index 482faf2d..98763092 100644
--- a/357_Synthcity/synthcity.eb
+++ b/357_Synthcity/synthcity.eb
@@ -178,8 +178,17 @@ local_xgbse_preinstallopts = (
 )
 
 exts_list = [
+    ('cython-test-exception-raiser', '1.0.2', {
+        'checksums': ['c5003a6321057a8222d2f5aed329fcdb5eb3bc615a9239f461bdf06c491fbdaa'],
+    }),
+    ('pyhamcrest', '2.1.0', {
+        'modulename': 'hamcrest',
+        'checksums': ['c6acbec0923d0cb7e72c22af1926f3e7c97b8e8d69fc7498eabacaf7c975bd9c'],
+    }),
+    ('service_identity', '24.1.0', {
+        'checksums': ['6829c9d62fb832c2e1c435629b0a8c476e1929881f28bee4d20bc24161009221'],
+    }),
     ('zope.interface', '6.3', {
-        # 'modulename': 'zope-interface',
         'checksums': ['f83d6b4b22262d9a826c3bd4b2fbfafe1d0000f085ef8e44cd1328eea274ae6a'],
     }),
     ('hyperlink', '21.0.0', {
@@ -365,41 +374,174 @@ exts_list = [
         'preinstallopts': "sed -i -e 's/pandas>=1.4,<2/pandas>=1.4/' -e 's/torch>=1.10.0,<2.0/torch>=1.10.0/' -e 's/numpy>=1.20, <1.24/numpy>=1.20/' -e 's/pydantic<2.0/pydantic/' -e 's/xgboost<2.0.0/xgboost/' -e 's/lifelines>=0.27,!= 0.27.5, <0.27.8/lifelines>=0.27,!= 0.27.5/' %(builddir)s/%(name)s/%(name)s-%(version)s/setup.cfg && ",
         'source_urls': ['https://github.com/vanderschaarlab/synthcity/archive/'],
         'sources': ['v%(version)s.tar.gz'],
+        'testinstall': True,
+        'runtest': 'pytest -vvvs -m "not slow" --durations=50',
         'checksums': ['122a8546cad35e3ac5c74cf81281252dec4dd3c7b1d7afe0f45ef785f89cc682'],
     }),
 ]
+
+# 'testinstall': True,
 # 'runtest': 'export PYTHONPATH=%(installdir)s/lib/python%(pyshortver)s/site-packages:$PYTHONPATH && pytest -vvvs -m "not slow" --durations=50',
 sanity_check_commands = [
-    'pytest -vvvs -m "not slow" --durations=50',
+    # 'pytest -vvvs -m "not slow" --durations=50',
     "python -c 'from synthcity.plugins import Plugins'",
     "python -c 'from decaf import DECAF, DataModule'"
 ]
 
 moduleclass = 'lib'
 
+# ERROR6:
+    # from test_log2.txt
+        # tests/plugins/core/models/survival_analysis/test_surv_xgb.py::test_train_prediction FAILED
+        
+        # tests/plugins/core/models/dag/test_dag_sanity.py::test_sanity 
+            # Training: |          | 0/? [00:00<?, ?it/s]
+            # Training:   0%|          | 0/1 [00:00<?, ?it/s]
+            # Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s] 
+            # Epoch 0: 100%|| 1/1 [00:03<00:00,  0.29it/s]
+            # Epoch 0: 100%|| 1/1 [00:03<00:00,  0.29it/s, v_num=2.01e+7, mse_loss=0.723, dsl_loss=3.660, total_loss=4.380]
+            # Epoch 0: 100%|| 1/1 [00:03<00:00,  0.29it/s, v_num=2.01e+7, mse_loss=0.723, dsl_loss=3.660, total_loss=4.380]
+            # Epoch 0: 100%|| 1/1 [00:03<00:00,  0.29it/s, v_num=2.01e+7, mse_loss=0.723, dsl_loss=3.660, total_loss=4.380]
+            # XFAIL
+        
+        # tests/plugins/core/models/time_series_survival/test_ts_surv_xgb.py::test_train_prediction[Transformer] FAILED
+        
+        # huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+        #     To disable this warning, you can either:
+	    #     - Avoid using `tokenizers` before the fork if possible
+	    #     - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+        #     /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages
+        #     /sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+        #     warnings.warn(msg, UserWarning)
+        #     FAILED
+        
+        # tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin1]
+        # 100%|| 1000/1000 [00:49<00:00, 20.18it/s]
+        # /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+        #     warnings.warn(msg, UserWarning)
+        # FAILED
+        
+        # same for: tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin2] FAILED
+        
+        # tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin1] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # FAILED
+        # tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin2] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # FAILED
+        # tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin0] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # FAILED
+        # tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin1] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # FAILED
+        # tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin2] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # FAILED
+        # tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin0] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # FAILED
+        # tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin1] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # FAILED
+        # tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin2] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # FAILED
+
+        # tests/plugins/time_series/test_timegan.py::test_plugin_generate_static_cond[source0] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 10%|         | 1/10 [00:01<00:09,  1.07s/it]
+        # 20%|        | 2/10 [00:02<00:08,  1.05s/it]
+        # 30%|       | 3/10 [00:03<00:07,  1.05s/it]
+        # 40%|      | 4/10 [00:04<00:06,  1.05s/it]
+        # 50%|     | 5/10 [00:05<00:05,  1.05s/it]
+        # 60%|    | 6/10 [00:06<00:04,  1.05s/it]
+        # 70%|   | 7/10 [00:07<00:03,  1.05s/it]
+        # 80%|  | 8/10 [00:08<00:02,  1.05s/it]
+        # 90%| | 9/10 [00:09<00:01,  1.05s/it]
+        # 100%|| 10/10 [00:10<00:00,  1.04s/it]
+        # 100%|| 10/10 [00:10<00:00,  1.05s/it]
+        # FAILED
+        # tests/plugins/time_series/test_timegan.py::test_plugin_generate_static_cond[source1] 
+        # 0%|          | 0/10 [00:00<?, ?it/s]
+        # 10%|         | 1/10 [00:00<00:05,  1.66it/s]
+        # 20%|        | 2/10 [00:01<00:04,  1.74it/s]
+        # 30%|       | 3/10 [00:01<00:03,  1.75it/s]
+        # 40%|      | 4/10 [00:02<00:03,  1.76it/s]
+        # 50%|     | 5/10 [00:02<00:02,  1.77it/s]
+        # 60%|    | 6/10 [00:03<00:02,  1.77it/s]
+        # 70%|   | 7/10 [00:03<00:01,  1.78it/s]
+        # 80%|  | 8/10 [00:04<00:01,  1.78it/s]
+        # 90%| | 9/10 [00:05<00:00,  1.78it/s]
+        # 100%|| 10/10 [00:05<00:00,  1.79it/s]
+        # 100%|| 10/10 [00:05<00:00,  1.77it/s]
+        # FAILED
+        
+        # E  AttributeError: `best_iteration` is only defined when early stopping is used.
+        # E  AttributeError: module 'pydantic' has no attribute 'pydantic_core'
+        # E  AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+        # E  ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (50,) + inhomogeneous part.
+        
+        # ============================================== short test summary info ===============================================
+        # FAILED tests/metrics/test_performance.py::test_evaluate_performance_time_series_survival[PerformanceEvaluatorXGB-test_plugin0]
+        # FAILED tests/plugins/core/test_schema.py::test_schema_fail - AttributeError: module 'pydantic' has no attribute 'py...
+        # FAILED tests/plugins/core/models/survival_analysis/test_surv_xgb.py::test_train_prediction - AttributeError: `best_...
+        # FAILED tests/plugins/core/models/time_series_survival/test_ts_surv_xgb.py::test_train_prediction[Transformer] - Att...
+        # FAILED tests/plugins/core/models/time_to_event/test_tte_xgb.py::test_train_prediction - AttributeError: `best_itera...
+        # FAILED tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin0] - RuntimeError: Training with multip...
+        # FAILED tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin1] - RuntimeError: Training with multip...
+        # FAILED tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin2] - RuntimeError: Training with multip...
+        # FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin1] - AttributeError: 'DPOptimizer'...
+        # FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin2] - AttributeError: 'DPOptimizer'...
+        # FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin0] - AttributeError: 'DPOptimizer...
+        # FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin1] - AttributeError: 'DPOptimizer...
+        # FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin2] - AttributeError: 'DPOptimizer...
+        # FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin0] - AttributeError: 'DPOpt...
+        # FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin1] - AttributeError: 'DPOpt...
+        # FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin2] - AttributeError: 'DPOpt...
+        # FAILED tests/plugins/time_series/test_timegan.py::test_plugin_generate_static_cond[source0] - ValueError: setting a...
+        # FAILED tests/plugins/time_series/test_timegan.py::test_plugin_generate_static_cond[source1] - ValueError: setting a...
+        # ======== 18 failed, 2141 passed, 13 skipped, 103 deselected, 2 xfailed, 2316 warnings in 15511.26s (4:18:31) =========
+        
 # ERROR5:
-    # from pytest:
-    # twisted 24.3.0 requires automat, ok
-    # twisted 24.3.0 requires constantly, ok
-    # twisted 24.3.0 requires hyperlink, ok
-    # twisted 24.3.0 requires zope-interface, ok
-    # No module named 'incremental' ok
-    # ModuleNotFoundError: No module named 'twisted' ok
-    # ModuleNotFoundError: No module named 'fixtures' ok
-    # ModuleNotFoundError: No module named 'hypothesis' OK
-    # ModuleNotFoundError: No module named 'testscenarios' ok
-    # ModuleNotFoundError: No module named 'testtools' ok
-    # ModuleNotFoundError: No module named 'evaluate' ok
-# ERROR4:
-    # maybe to postinstallopts?
-    # ??? add "export PYTHONPATH=%(installdir)s/lib/python%(pyshortver)s/site-packages:$PYTHONPATH &&" before pytest?
-        # -> NO there is no synthcity in site-packages yet
-    # ??? -> add pytest cmd to sanity check cmds
-    # = 2024-07-16 15:03:20,115 build_log.py:171 ERROR EasyBuild crashed with an error (at easybuild/easybuild-framework/easybuild/base/exceptions.py:126 in __init__): cmd "  pytest -vvvs -m "n slow" --durations=50 " exited with exit code 4 and output:
-    # ImportError while loading conftest '/tmp/vsc47063/easybuild/build/synthcity/0.2.10/foss-2023a/synthcity/synthcity-0.2.10/tests/conftest.py'.
-    # tests/conftest.py:10: in <module>
-    #     from synthcity.utils.reproducibility import clear_cache, enable_reproducible_results
-    # E   ModuleNotFoundError: No module named 'synthcity'
+    # -> Delete this - seems is not from synthcity but fro others packages
+    # Pytest in sanity check cmds:
+        # ModuleNotFoundError: No module named 'cython_test_exception_raiser' ok
+        # ModuleNotFoundError: No module named 'hamcrest' ok
+        # No module named 'service_identity ok
+        # twisted 24.3.0 requires automat, ok
+        # twisted 24.3.0 requires constantly, ok
+        # twisted 24.3.0 requires hyperlink, ok
+        # twisted 24.3.0 requires zope-interface, ok
+        # No module named 'incremental' ok
+        # ModuleNotFoundError: No module named 'twisted' ok
+        # ModuleNotFoundError: No module named 'fixtures' ok
+        # ModuleNotFoundError: No module named 'hypothesis' OK
+        # ModuleNotFoundError: No module named 'testscenarios' ok
+        # ModuleNotFoundError: No module named 'testtools' ok
+        # ModuleNotFoundError: No module named 'evaluate' ok
+# ERROR4: OK
+    # try runtest + 'testinstall': True
+        # -> WORKS
+    # maybe to postinstallcmds?
+    # add to runtest: "export PYTHONPATH=%(installdir)s/lib/python%(pyshortver)s/site-packages:$PYTHONPATH &&" before pytest?
+        # NO there is no synthcity in site-packages yet
+    # add pytest cmd to sanity check cmds
+        # seems it tests all the packages not only synthcity -> errors about modules etc
+    # when runtest = 'pytest -vvvs -m "not slow" --durations=50'
+        # = 2024-07-16 15:03:20,115 build_log.py:171 ERROR EasyBuild crashed with an error (at easybuild/easybuild-framework/easybuild/base/exceptions.py:126 in __init__): cmd "  pytest -vvvs -m "n slow" --durations=50 " exited with exit code 4 and output:
+        # ImportError while loading conftest '/tmp/vsc47063/easybuild/build/synthcity/0.2.10/foss-2023a/synthcity/synthcity-0.2.10/tests/conftest.py'.
+        # tests/conftest.py:10: in <module>
+        #     from synthcity.utils.reproducibility import clear_cache, enable_reproducible_results
+        # E   ModuleNotFoundError: No module named 'synthcity'
 # ERROR3: OK
     # -> add coverage to deps
     # -> put runtest to exts - synthcity ext, not as runtest = ...
diff --git a/357_Synthcity/test_log2.txt b/357_Synthcity/test_log2.txt
new file mode 100644
index 00000000..ec0666d3
--- /dev/null
+++ b/357_Synthcity/test_log2.txt
@@ -0,0 +1,40386 @@
+# output for command: export PYTHONPATH=/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages:$PYTHONPATH &&  pytest -vvvs -m "not slow" --durations=50
+
+================================================ test session starts =================================================
+platform linux -- Python 3.11.3, pytest-7.4.0, pluggy-1.2.0 -- /apps/gent/RHEL8/cascadelake-ib/software/Python/3.11.3-GCCcore-12.3.0/bin/python
+cachedir: .pytest_cache
+benchmark: 4.0.0 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
+hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/tmp/vsc47063/easybuild/build/synthcity/0.2.10/foss-2023a/synthcity/synthcity-0.2.10/.hypothesis/examples')
+rootdir: /tmp/vsc47063/easybuild/build/synthcity/0.2.10/foss-2023a/synthcity/synthcity-0.2.10
+configfile: setup.cfg
+testpaths: tests
+plugins: cov-5.0.0, benchmark-4.0.0, xprocess-1.0.2, hypothesis-6.82.0, anyio-3.7.1, mock-3.11.1, xdist-3.3.1
+collecting ... [2024-07-18T16:39:09.026539+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.144144+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.226922+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.230814+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.235274+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.238942+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.243006+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.246666+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.250728+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.254382+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.258169+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.270898+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.274546+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.287046+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.334289+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.337943+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.341494+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.345042+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.348578+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.422209+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.425891+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.429479+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.433089+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.436661+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.440235+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.443833+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.447781+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.451359+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:33.472734+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[KeOps] Warning : Cuda libraries were not detected on the system or could not be loaded ; using cpu only mode
+Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
+Failed to download (trying next):
+HTTP Error 403: Forbidden
+
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz
+
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+Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw
+
+Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
+Failed to download (trying next):
+HTTP Error 403: Forbidden
+
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz
+
+  0%|          | 0/28881 [00:00<?, ?it/s]
+100%|| 28881/28881 [00:00<00:00, 335081.63it/s]
+Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw
+
+Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
+Failed to download (trying next):
+HTTP Error 403: Forbidden
+
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz
+
+  0%|          | 0/1648877 [00:00<?, ?it/s]
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+100%|| 1648877/1648877 [00:02<00:00, 679916.14it/s]
+Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw
+
+Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
+Failed to download (trying next):
+HTTP Error 403: Forbidden
+
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz
+
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+[2024-07-18T16:39:55.359008+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.367940+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.375283+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.382626+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.389979+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.397291+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.404626+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.457205+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.464284+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.470978+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.477646+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.484338+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.491096+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.497867+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.532290+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.539271+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.545927+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.552560+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.559208+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.565834+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.619926+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.627985+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.635710+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.643413+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.651092+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.658848+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:39:55.666595+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw
+
+
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.02, val=0.987]
+Epoch:   0%|          | 1/1000 [00:00<05:24,  3.08it/s, loss=1.02, val=0.987]
+Epoch:   0%|          | 1/1000 [00:00<05:24,  3.08it/s, loss=0.969, val=0.997]
+Epoch:   0%|          | 2/1000 [00:00<04:39,  3.57it/s, loss=0.969, val=0.997]
+Epoch:   0%|          | 2/1000 [00:00<04:39,  3.57it/s, loss=0.957, val=0.948]
+Epoch:   0%|          | 3/1000 [00:00<04:15,  3.90it/s, loss=0.957, val=0.948]
+Epoch:   0%|          | 3/1000 [00:01<04:15,  3.90it/s, loss=0.914, val=0.997]
+Epoch:   0%|          | 4/1000 [00:01<04:06,  4.05it/s, loss=0.914, val=0.997]
+Epoch:   0%|          | 4/1000 [00:01<04:06,  4.05it/s, loss=0.859, val=0.943]
+Epoch:   0%|          | 5/1000 [00:01<04:07,  4.02it/s, loss=0.859, val=0.943]
+Epoch:   0%|          | 5/1000 [00:01<04:07,  4.02it/s, loss=0.797, val=0.922]
+Epoch:   1%|          | 6/1000 [00:01<04:02,  4.10it/s, loss=0.797, val=0.922]
+Epoch:   1%|          | 6/1000 [00:01<04:02,  4.10it/s, loss=0.772, val=0.952]
+Epoch:   1%|          | 7/1000 [00:01<04:07,  4.02it/s, loss=0.772, val=0.952]
+Epoch:   1%|          | 7/1000 [00:02<04:07,  4.02it/s, loss=0.757, val=0.987]
+Epoch:   1%|          | 8/1000 [00:02<04:00,  4.13it/s, loss=0.757, val=0.987]
+Epoch:   1%|          | 8/1000 [00:02<04:00,  4.13it/s, loss=0.746, val=0.987]
+Epoch:   1%|          | 9/1000 [00:02<03:54,  4.23it/s, loss=0.746, val=0.987]
+Epoch:   1%|          | 9/1000 [00:02<03:54,  4.23it/s, loss=0.719, val=0.995]
+Epoch:   1%|          | 10/1000 [00:02<03:53,  4.23it/s, loss=0.719, val=0.995]
+Epoch:   1%|          | 10/1000 [00:02<03:53,  4.23it/s, loss=0.7, val=0.988]  
+Epoch:   1%|          | 11/1000 [00:02<03:52,  4.25it/s, loss=0.7, val=0.988]
+Epoch:   1%|          | 11/1000 [00:02<03:52,  4.25it/s, loss=0.688, val=0.968]
+Epoch:   1%|          | 12/1000 [00:02<03:51,  4.26it/s, loss=0.688, val=0.968]
+Epoch:   1%|          | 12/1000 [00:03<03:51,  4.26it/s, loss=0.68, val=1]     
+Epoch:   1%|         | 13/1000 [00:03<03:52,  4.25it/s, loss=0.68, val=1]
+Epoch:   1%|         | 13/1000 [00:03<03:52,  4.25it/s, loss=0.678, val=1]
+Epoch:   1%|         | 14/1000 [00:03<03:51,  4.26it/s, loss=0.678, val=1]
+Epoch:   1%|         | 14/1000 [00:03<03:51,  4.26it/s, loss=0.672, val=0.997]
+Epoch:   2%|         | 15/1000 [00:03<03:53,  4.22it/s, loss=0.672, val=0.997]
+Epoch:   2%|         | 15/1000 [00:03<03:53,  4.22it/s, loss=0.661, val=1]    
+Epoch:   2%|         | 16/1000 [00:03<03:51,  4.26it/s, loss=0.661, val=1]
+Epoch:   2%|         | 16/1000 [00:04<03:51,  4.26it/s, loss=0.655, val=1]
+Epoch:   2%|         | 17/1000 [00:04<03:51,  4.24it/s, loss=0.655, val=1]
+Epoch:   2%|         | 17/1000 [00:04<03:51,  4.24it/s, loss=0.649, val=0.967]
+Epoch:   2%|         | 18/1000 [00:04<03:50,  4.25it/s, loss=0.649, val=0.967]
+Epoch:   2%|         | 18/1000 [00:04<03:50,  4.25it/s, loss=0.64, val=0.983] 
+Epoch:   2%|         | 19/1000 [00:04<03:48,  4.30it/s, loss=0.64, val=0.983]
+Epoch:   2%|         | 19/1000 [00:04<03:48,  4.30it/s, loss=0.634, val=1]   
+Epoch:   2%|         | 20/1000 [00:04<03:50,  4.25it/s, loss=0.634, val=1]
+Epoch:   2%|         | 20/1000 [00:05<03:50,  4.25it/s, loss=0.63, val=0.967]
+Epoch:   2%|         | 21/1000 [00:05<03:50,  4.25it/s, loss=0.63, val=0.967]
+Epoch:   2%|         | 21/1000 [00:05<03:50,  4.25it/s, loss=0.629, val=0.88]
+Epoch:   2%|         | 22/1000 [00:05<03:48,  4.28it/s, loss=0.629, val=0.88]
+Epoch:   2%|         | 22/1000 [00:05<03:48,  4.28it/s, loss=0.624, val=0.948]
+Epoch:   2%|         | 23/1000 [00:05<03:57,  4.12it/s, loss=0.624, val=0.948]
+Epoch:   2%|         | 23/1000 [00:05<03:57,  4.12it/s, loss=0.62, val=1]     
+Epoch:   2%|         | 24/1000 [00:05<03:53,  4.18it/s, loss=0.62, val=1]
+Epoch:   2%|         | 24/1000 [00:06<03:53,  4.18it/s, loss=0.616, val=1]
+Epoch:   2%|         | 25/1000 [00:06<03:52,  4.19it/s, loss=0.616, val=1]
+Epoch:   2%|         | 25/1000 [00:06<03:52,  4.19it/s, loss=0.613, val=0.997]
+Epoch:   3%|         | 26/1000 [00:06<03:49,  4.24it/s, loss=0.613, val=0.997]
+Epoch:   3%|         | 26/1000 [00:06<03:49,  4.24it/s, loss=0.607, val=0.997]
+Epoch:   3%|         | 27/1000 [00:06<03:51,  4.21it/s, loss=0.607, val=0.997]
+Epoch:   3%|         | 27/1000 [00:06<03:51,  4.21it/s, loss=0.605, val=0.973]
+Epoch:   3%|         | 28/1000 [00:06<03:52,  4.18it/s, loss=0.605, val=0.973]
+Epoch:   3%|         | 28/1000 [00:06<03:52,  4.18it/s, loss=0.602, val=0.963]
+Epoch:   3%|         | 29/1000 [00:06<03:47,  4.26it/s, loss=0.602, val=0.963]
+Epoch:   3%|         | 29/1000 [00:07<03:47,  4.26it/s, loss=0.597, val=0.983]
+Epoch:   3%|         | 30/1000 [00:07<03:48,  4.25it/s, loss=0.597, val=0.983]
+Epoch:   3%|         | 30/1000 [00:07<03:48,  4.25it/s, loss=0.592, val=0.953]
+Epoch:   3%|         | 31/1000 [00:07<03:48,  4.24it/s, loss=0.592, val=0.953]
+Epoch:   3%|         | 31/1000 [00:07<03:48,  4.24it/s, loss=0.593, val=1]    
+Epoch:   3%|         | 32/1000 [00:07<03:49,  4.21it/s, loss=0.593, val=1]
+Epoch:   3%|         | 32/1000 [00:07<03:49,  4.21it/s, loss=0.591, val=0.983]
+Epoch:   3%|         | 33/1000 [00:07<03:48,  4.23it/s, loss=0.591, val=0.983]
+Epoch:   3%|         | 33/1000 [00:08<03:48,  4.23it/s, loss=0.588, val=0.923]
+Epoch:   3%|         | 34/1000 [00:08<03:45,  4.28it/s, loss=0.588, val=0.923]
+Epoch:   3%|         | 34/1000 [00:08<03:45,  4.28it/s, loss=0.586, val=0.99] 
+Epoch:   4%|         | 35/1000 [00:08<03:44,  4.29it/s, loss=0.586, val=0.99]
+Epoch:   4%|         | 35/1000 [00:08<03:44,  4.29it/s, loss=0.583, val=0.95]
+Epoch:   4%|         | 36/1000 [00:08<03:42,  4.34it/s, loss=0.583, val=0.95]
+Epoch:   4%|         | 36/1000 [00:08<03:42,  4.34it/s, loss=0.577, val=0.973]
+Epoch:   4%|         | 37/1000 [00:08<03:42,  4.33it/s, loss=0.577, val=0.973]
+Epoch:   4%|         | 37/1000 [00:09<03:42,  4.33it/s, loss=0.572, val=0.99] 
+Epoch:   4%|         | 38/1000 [00:09<03:42,  4.33it/s, loss=0.572, val=0.99]
+Epoch:   4%|         | 38/1000 [00:09<03:42,  4.33it/s, loss=0.57, val=0.952]
+Epoch:   4%|         | 39/1000 [00:09<03:42,  4.33it/s, loss=0.57, val=0.952]
+Epoch:   4%|         | 39/1000 [00:09<03:42,  4.33it/s, loss=0.568, val=0.987]
+Epoch:   4%|         | 40/1000 [00:09<03:43,  4.29it/s, loss=0.568, val=0.987]
+Epoch:   4%|         | 40/1000 [00:09<03:43,  4.29it/s, loss=0.565, val=0.988]
+Epoch:   4%|         | 41/1000 [00:09<03:39,  4.36it/s, loss=0.565, val=0.988]
+Epoch:   4%|         | 41/1000 [00:09<03:39,  4.36it/s, loss=0.563, val=0.938]
+Epoch:   4%|         | 42/1000 [00:09<03:40,  4.35it/s, loss=0.563, val=0.938]
+Epoch:   4%|         | 42/1000 [00:10<03:40,  4.35it/s, loss=0.561, val=0.87] 
+Epoch:   4%|         | 43/1000 [00:10<03:41,  4.33it/s, loss=0.561, val=0.87]
+Epoch:   4%|         | 43/1000 [00:10<03:41,  4.33it/s, loss=0.559, val=1]   
+Epoch:   4%|         | 44/1000 [00:10<03:42,  4.30it/s, loss=0.559, val=1]
+Epoch:   4%|         | 44/1000 [00:10<03:42,  4.30it/s, loss=0.558, val=0.95]
+Epoch:   4%|         | 45/1000 [00:10<03:41,  4.32it/s, loss=0.558, val=0.95]
+Epoch:   4%|         | 45/1000 [00:10<03:41,  4.32it/s, loss=0.554, val=0.958]
+Epoch:   5%|         | 46/1000 [00:10<03:41,  4.31it/s, loss=0.554, val=0.958]
+Epoch:   5%|         | 46/1000 [00:11<03:41,  4.31it/s, loss=0.551, val=0.917]
+Epoch:   5%|         | 47/1000 [00:11<03:41,  4.30it/s, loss=0.551, val=0.917]
+Epoch:   5%|         | 47/1000 [00:11<03:41,  4.30it/s, loss=0.549, val=0.972]
+Epoch:   5%|         | 48/1000 [00:11<03:43,  4.26it/s, loss=0.549, val=0.972]
+Epoch:   5%|         | 48/1000 [00:11<03:43,  4.26it/s, loss=0.546, val=0.972]
+Epoch:   5%|         | 49/1000 [00:11<03:40,  4.32it/s, loss=0.546, val=0.972]
+Epoch:   5%|         | 49/1000 [00:11<03:40,  4.32it/s, loss=0.543, val=0.87] 
+Epoch:   5%|         | 50/1000 [00:11<03:40,  4.31it/s, loss=0.543, val=0.87]
+Epoch:   5%|         | 50/1000 [00:12<03:40,  4.31it/s, loss=0.54, val=0.963]
+Epoch:   5%|         | 51/1000 [00:12<03:40,  4.31it/s, loss=0.54, val=0.963]
+Epoch:   5%|         | 51/1000 [00:12<03:40,  4.31it/s, loss=0.538, val=0.91]
+Epoch:   5%|         | 52/1000 [00:12<03:43,  4.25it/s, loss=0.538, val=0.91]
+Epoch:   5%|         | 52/1000 [00:12<03:43,  4.25it/s, loss=0.536, val=0.997]
+Epoch:   5%|         | 52/1000 [00:12<03:48,  4.15it/s, loss=0.536, val=0.997]
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+[2024-07-18T16:40:09.497246+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.505679+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.564921+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.574284+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.582796+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.591220+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.638175+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.646268+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.654028+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.661766+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.713624+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.720671+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.727426+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.734186+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.740958+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.747753+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.754588+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.791906+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.798815+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.805414+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.811996+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.818631+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.856153+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.864220+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.871963+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.879693+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.887463+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.895253+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.903053+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.942290+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.952171+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.961736+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.971271+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:09.980830+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.005486+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.014337+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.022846+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.031348+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.039851+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.094815+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.101700+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.108379+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.115050+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.121717+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.128386+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.172063+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.178732+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.185078+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.191425+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.542007+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.549564+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.556768+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.563904+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.608696+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.615455+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.621889+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.628301+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.659081+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.662095+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.668006+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.673770+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.678312+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.681114+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.686893+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.692664+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.697202+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.700000+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.705807+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.711576+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.716098+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.718893+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.724663+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.730416+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.776134+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.782652+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.789065+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.795471+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.820936+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+[2024-07-18T16:40:10.827732+0200][1743064][CRITICAL] module disabled: /tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py
+collected 2277 items / 103 deselected / 2174 selected
+
+tests/test_logger.py::test_loglevel[ERROR] PASSED
+tests/test_logger.py::test_loglevel[DEBUG] PASSED
+tests/test_logger.py::test_loglevel[CRITICAL] PASSED
+tests/test_logger.py::test_log_cbk[ERROR-error] [2024-07-18T16:40:11.260963+0200][1743064][ERROR] test
+PASSED
+tests/test_logger.py::test_log_cbk[DEBUG-debug] [2024-07-18T16:40:11.273428+0200][1743064][DEBUG] test
+PASSED
+tests/test_logger.py::test_log_cbk[WARNING-warning] [2024-07-18T16:40:11.285857+0200][1743064][WARNING] test
+PASSED
+tests/test_logger.py::test_log_cbk[CRITICAL-critical] [2024-07-18T16:40:11.298270+0200][1743064][CRITICAL] test
+PASSED
+tests/benchmarks/test_benchmarks.py::test_benchmark_sanity 
+Comparatives
+                                           test1        test2
+sanity.common_rows_proportion.score  0.0 +/- 0.0  0.0 +/- 0.0
+PASSED
+tests/benchmarks/test_benchmarks.py::test_benchmark_augmentation 
+Comparatives
+Empty DataFrame
+Columns: [test1, test2]
+Index: []
+PASSED
+tests/benchmarks/test_benchmarks.py::test_benchmark_invalid_plugin PASSED
+tests/benchmarks/test_benchmarks.py::test_benchmark_invalid_metric PASSED
+tests/benchmarks/test_benchmarks.py::test_benchmark_custom_target PASSED
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis 
+Plugin : test1
+                                                   min       max      mean  \
+performance.linear_model.gt.c_index           0.565913  0.565913  0.565913   
+performance.linear_model.gt.brier_score       0.147064  0.147064  0.147064   
+performance.linear_model.syn_id.c_index       0.000000  0.000000  0.000000   
+performance.linear_model.syn_id.brier_score   1.000000  1.000000  1.000000   
+performance.linear_model.syn_ood.c_index      0.000000  0.000000  0.000000   
+performance.linear_model.syn_ood.brier_score  1.000000  1.000000  1.000000   
+
+                                              stddev    median  iqr rounds  \
+performance.linear_model.gt.c_index              0.0  0.565913  0.0      3   
+performance.linear_model.gt.brier_score          0.0  0.147064  0.0      3   
+performance.linear_model.syn_id.c_index          0.0  0.000000  0.0      3   
+performance.linear_model.syn_id.brier_score      0.0  1.000000  0.0      3   
+performance.linear_model.syn_ood.c_index         0.0  0.000000  0.0      3   
+performance.linear_model.syn_ood.brier_score     0.0  1.000000  0.0      3   
+
+                                             errors  durations  
+performance.linear_model.gt.c_index               0       0.13  
+performance.linear_model.gt.brier_score           0       0.13  
+performance.linear_model.syn_id.c_index           0       0.13  
+performance.linear_model.syn_id.brier_score       0       0.13  
+performance.linear_model.syn_ood.c_index          0       0.13  
+performance.linear_model.syn_ood.brier_score      0       0.13  
+
+
+Comparatives
+                                                                   test1  \
+performance.linear_model.gt.c_index                        0.566 +/- 0.0   
+performance.linear_model.gt.brier_score                    0.147 +/- 0.0   
+performance.linear_model.syn_id.c_index                      0.0 +/- 0.0   
+performance.linear_model.syn_id.brier_score                  1.0 +/- 0.0   
+performance.linear_model.syn_ood.c_index                     0.0 +/- 0.0   
+performance.linear_model.syn_ood.brier_score                 1.0 +/- 0.0   
+performance.linear_model_augmentation.gt.c_index           0.643 +/- 0.0   
+performance.linear_model_augmentation.gt.brier_score       0.125 +/- 0.0   
+performance.linear_model_augmentation.syn_id.c_index       0.418 +/- 0.0   
+performance.linear_model_augmentation.syn_id.brier_score   0.132 +/- 0.0   
+performance.linear_model_augmentation.syn_ood.c_index      0.563 +/- 0.0   
+performance.linear_model_augmentation.syn_ood.brier_score  0.183 +/- 0.0   
+
+                                                                     test2  
+performance.linear_model.gt.c_index                          0.566 +/- 0.0  
+performance.linear_model.gt.brier_score                      0.147 +/- 0.0  
+performance.linear_model.syn_id.c_index                    0.675 +/- 0.047  
+performance.linear_model.syn_id.brier_score                0.128 +/- 0.001  
+performance.linear_model.syn_ood.c_index                   0.267 +/- 0.094  
+performance.linear_model.syn_ood.brier_score               0.043 +/- 0.005  
+performance.linear_model_augmentation.gt.c_index             0.643 +/- 0.0  
+performance.linear_model_augmentation.gt.brier_score         0.125 +/- 0.0  
+performance.linear_model_augmentation.syn_id.c_index       0.829 +/- 0.002  
+performance.linear_model_augmentation.syn_id.brier_score   0.092 +/- 0.001  
+performance.linear_model_augmentation.syn_ood.c_index        0.422 +/- 0.0  
+performance.linear_model_augmentation.syn_ood.brier_score  0.173 +/- 0.001  
+PASSED
+tests/benchmarks/test_benchmarks.py::test_benchmark_workspace_cache PASSED
+tests/benchmarks/test_benchmarks.py::test_benchmark_added_plugin PASSED
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+PASSED
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+PASSED
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+PASSED
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+PASSED
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+100%|| 10/10 [00:00<00:00, 13.78it/s]
+PASSED
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+PASSED
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+PASSED
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+PASSED
+tests/plugins/core/models/test_normalizing_flows.py::test_nf_fit_generate[quadratic-autoregressive-lu-standard_normal] 
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+PASSED
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+PASSED
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+100%|| 10/10 [00:00<00:00, 20.53it/s]
+PASSED
+tests/plugins/core/models/test_normalizing_flows.py::test_nf_fit_generate[rq-autoregressive-permutation-standard_normal] 
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+ 60%|    | 6/10 [00:00<00:00, 24.71it/s]
+ 90%| | 9/10 [00:00<00:00, 24.71it/s]
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+PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_sanity[TabularEncoder] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_sanity[TimeSeriesTabularEncoder] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_fit_no_discrete_param[4] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_fit_no_discrete_param[10] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_fit_discrete_param[4] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_fit_discrete_param[10] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_fit_transform[4] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_fit_transform[10] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_encoder_inverse_transform[20] PASSED
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+tests/plugins/core/models/test_tabular_encoder.py::test_bin_encoder PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_ts_encoder_fit[SineDataloader] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_ts_encoder_fit[GoogleStocksDataloader] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_ts_encoder_fit_transform[SineDataloader] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_ts_encoder_fit_transform[GoogleStocksDataloader] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_ts_encoder_inverse_transform[SineDataloader] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_ts_encoder_inverse_transform[GoogleStocksDataloader] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_ts_encoder_activation_layout[SineDataloader] PASSED
+tests/plugins/core/models/test_tabular_encoder.py::test_ts_encoder_activation_layout[GoogleStocksDataloader] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_network_config PASSED
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+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-True-0-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-True-0-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-True-0.5-relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-True-0.5-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-True-0.5-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-True-0.2-relu] PASSED
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+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-True-0.2-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-False-0-relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-False-0-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-False-0-leaky_relu] PASSED
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+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-False-0.5-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-False-0.5-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-False-0.2-relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-False-0.2-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.001-False-0.2-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0-relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0.5-relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0.5-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0.5-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0.2-relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0.2-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-True-0.2-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-False-0-relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-False-0-elu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-False-0-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-False-0.5-relu] PASSED
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+tests/plugins/core/models/test_tabular_gan.py::test_basic_network[0.0003-False-0.2-leaky_relu] PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_gan_classification 
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+PASSED
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+PASSED
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+100%|| 10/10 [00:16<00:00,  1.61s/it]
+PASSED
+tests/plugins/core/models/test_tabular_gan.py::test_gan_generation_with_early_stopping[patience_metric0] 
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+  2%|         | 18/1000 [00:01<00:56, 17.28it/s]
+  2%|         | 20/1000 [00:01<00:56, 17.33it/s]
+  2%|         | 22/1000 [00:01<00:55, 17.75it/s]
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+ 20%|        | 198/1000 [00:13<00:45, 17.58it/s]
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+PASSED
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+  0%|          | 0/100 [00:00<?, ?it/s]
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+ 45%|     | 45/100 [00:13<00:17,  3.23it/s]
+ 46%|     | 46/100 [00:14<00:16,  3.24it/s]
+ 47%|     | 47/100 [00:14<00:16,  3.24it/s]
+ 48%|     | 48/100 [00:14<00:16,  3.25it/s]
+ 49%|     | 49/100 [00:15<00:15,  3.20it/s]
+ 50%|     | 50/100 [00:15<00:15,  3.18it/s]
+ 51%|     | 51/100 [00:15<00:15,  3.18it/s]
+ 52%|    | 52/100 [00:16<00:15,  3.19it/s]
+ 53%|    | 53/100 [00:16<00:14,  3.20it/s]
+ 54%|    | 54/100 [00:16<00:14,  3.22it/s]
+ 55%|    | 55/100 [00:17<00:14,  3.20it/s]
+ 56%|    | 56/100 [00:17<00:13,  3.22it/s]
+ 57%|    | 57/100 [00:17<00:13,  3.20it/s]
+ 58%|    | 58/100 [00:18<00:13,  3.20it/s]
+ 59%|    | 59/100 [00:18<00:12,  3.17it/s]
+ 60%|    | 60/100 [00:18<00:12,  3.17it/s]
+ 61%|    | 61/100 [00:19<00:12,  3.16it/s]
+ 62%|   | 62/100 [00:19<00:11,  3.20it/s]
+ 63%|   | 63/100 [00:19<00:11,  3.21it/s]
+ 64%|   | 64/100 [00:19<00:11,  3.22it/s]
+ 65%|   | 65/100 [00:20<00:10,  3.22it/s]
+ 66%|   | 66/100 [00:20<00:10,  3.22it/s]
+ 67%|   | 67/100 [00:20<00:10,  3.24it/s]
+ 68%|   | 68/100 [00:21<00:09,  3.23it/s]
+ 69%|   | 69/100 [00:21<00:09,  3.22it/s]
+ 70%|   | 70/100 [00:21<00:09,  3.23it/s]
+ 71%|   | 71/100 [00:22<00:09,  3.18it/s]
+ 72%|  | 72/100 [00:22<00:08,  3.23it/s]
+ 73%|  | 73/100 [00:22<00:08,  3.23it/s]
+ 74%|  | 74/100 [00:23<00:08,  3.22it/s]
+ 75%|  | 75/100 [00:23<00:07,  3.23it/s]
+ 76%|  | 76/100 [00:23<00:07,  3.23it/s]
+ 77%|  | 77/100 [00:23<00:07,  3.25it/s]
+ 78%|  | 78/100 [00:24<00:06,  3.27it/s]
+ 79%|  | 79/100 [00:24<00:06,  3.25it/s]
+ 80%|  | 80/100 [00:24<00:06,  3.25it/s]
+ 81%|  | 81/100 [00:25<00:05,  3.19it/s]
+ 82%| | 82/100 [00:25<00:05,  3.17it/s]
+ 83%| | 83/100 [00:25<00:05,  3.18it/s]
+ 84%| | 84/100 [00:26<00:05,  3.19it/s]
+ 85%| | 85/100 [00:26<00:04,  3.19it/s]
+ 86%| | 86/100 [00:26<00:04,  3.20it/s]
+ 87%| | 87/100 [00:27<00:04,  3.21it/s]
+ 88%| | 88/100 [00:27<00:03,  3.21it/s]
+ 89%| | 89/100 [00:27<00:03,  3.19it/s]
+ 90%| | 90/100 [00:28<00:03,  3.23it/s]
+ 91%| | 91/100 [00:28<00:02,  3.16it/s]
+ 92%|| 92/100 [00:28<00:02,  3.17it/s]
+ 93%|| 93/100 [00:28<00:02,  3.18it/s]
+ 94%|| 94/100 [00:29<00:01,  3.18it/s]
+ 95%|| 95/100 [00:29<00:01,  3.21it/s]
+ 96%|| 96/100 [00:29<00:01,  3.21it/s]
+ 97%|| 97/100 [00:30<00:00,  3.23it/s]
+ 98%|| 98/100 [00:30<00:00,  3.20it/s]
+ 99%|| 99/100 [00:30<00:00,  3.19it/s]
+100%|| 100/100 [00:31<00:00,  3.18it/s]
+100%|| 100/100 [00:31<00:00,  3.21it/s]
+PASSED
+tests/plugins/core/models/test_tabular_vae.py::test_vae_classification[robust_divergence] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:35,  2.76it/s]
+  2%|         | 2/100 [00:00<00:32,  3.00it/s]
+  3%|         | 3/100 [00:00<00:31,  3.08it/s]
+  4%|         | 4/100 [00:01<00:30,  3.13it/s]
+  5%|         | 5/100 [00:01<00:30,  3.14it/s]
+  6%|         | 6/100 [00:01<00:29,  3.17it/s]
+  7%|         | 7/100 [00:02<00:29,  3.15it/s]
+  8%|         | 8/100 [00:02<00:29,  3.15it/s]
+  9%|         | 9/100 [00:02<00:28,  3.18it/s]
+ 10%|         | 10/100 [00:03<00:28,  3.19it/s]
+ 11%|         | 11/100 [00:03<00:28,  3.18it/s]
+ 12%|        | 12/100 [00:03<00:27,  3.19it/s]
+ 13%|        | 13/100 [00:04<00:27,  3.21it/s]
+ 14%|        | 14/100 [00:04<00:27,  3.18it/s]
+ 15%|        | 15/100 [00:04<00:26,  3.19it/s]
+ 16%|        | 16/100 [00:05<00:26,  3.18it/s]
+ 17%|        | 17/100 [00:05<00:26,  3.16it/s]
+ 18%|        | 18/100 [00:05<00:26,  3.15it/s]
+ 19%|        | 19/100 [00:06<00:25,  3.17it/s]
+ 20%|        | 20/100 [00:06<00:25,  3.17it/s]
+ 21%|        | 21/100 [00:06<00:25,  3.14it/s]
+ 22%|       | 22/100 [00:06<00:24,  3.16it/s]
+ 23%|       | 23/100 [00:07<00:24,  3.20it/s]
+ 24%|       | 24/100 [00:07<00:23,  3.23it/s]
+ 25%|       | 25/100 [00:07<00:23,  3.22it/s]
+ 26%|       | 26/100 [00:08<00:23,  3.21it/s]
+ 27%|       | 27/100 [00:08<00:23,  3.17it/s]
+ 28%|       | 28/100 [00:08<00:22,  3.20it/s]
+ 29%|       | 29/100 [00:09<00:22,  3.20it/s]
+ 30%|       | 30/100 [00:09<00:21,  3.23it/s]
+ 31%|       | 31/100 [00:09<00:21,  3.22it/s]
+ 32%|      | 32/100 [00:10<00:21,  3.19it/s]
+ 33%|      | 33/100 [00:10<00:21,  3.16it/s]
+ 34%|      | 34/100 [00:10<00:20,  3.18it/s]
+ 35%|      | 35/100 [00:11<00:20,  3.18it/s]
+ 36%|      | 36/100 [00:11<00:20,  3.16it/s]
+ 37%|      | 37/100 [00:11<00:20,  3.13it/s]
+ 38%|      | 38/100 [00:11<00:19,  3.13it/s]
+ 39%|      | 39/100 [00:12<00:19,  3.16it/s]
+ 40%|      | 40/100 [00:12<00:18,  3.19it/s]
+ 41%|      | 41/100 [00:12<00:18,  3.14it/s]
+ 42%|     | 42/100 [00:13<00:18,  3.16it/s]
+ 43%|     | 43/100 [00:13<00:18,  3.16it/s]
+ 44%|     | 44/100 [00:13<00:17,  3.13it/s]
+ 45%|     | 45/100 [00:14<00:17,  3.13it/s]
+ 46%|     | 46/100 [00:14<00:17,  3.14it/s]
+ 47%|     | 47/100 [00:14<00:16,  3.15it/s]
+ 48%|     | 48/100 [00:15<00:16,  3.13it/s]
+ 49%|     | 49/100 [00:15<00:16,  3.15it/s]
+ 50%|     | 50/100 [00:15<00:15,  3.14it/s]
+ 51%|     | 51/100 [00:16<00:15,  3.14it/s]
+ 52%|    | 52/100 [00:16<00:15,  3.16it/s]
+ 53%|    | 53/100 [00:16<00:14,  3.14it/s]
+ 54%|    | 54/100 [00:17<00:14,  3.15it/s]
+ 55%|    | 55/100 [00:17<00:14,  3.19it/s]
+ 56%|    | 56/100 [00:17<00:13,  3.20it/s]
+ 57%|    | 57/100 [00:18<00:13,  3.20it/s]
+ 58%|    | 58/100 [00:18<00:13,  3.20it/s]
+ 59%|    | 59/100 [00:18<00:12,  3.21it/s]
+ 60%|    | 60/100 [00:18<00:12,  3.22it/s]
+ 61%|    | 61/100 [00:19<00:12,  3.18it/s]
+ 62%|   | 62/100 [00:19<00:12,  3.16it/s]
+ 63%|   | 63/100 [00:19<00:11,  3.18it/s]
+ 64%|   | 64/100 [00:20<00:11,  3.17it/s]
+ 65%|   | 65/100 [00:20<00:11,  3.17it/s]
+ 66%|   | 66/100 [00:20<00:10,  3.14it/s]
+ 67%|   | 67/100 [00:21<00:10,  3.16it/s]
+ 68%|   | 68/100 [00:21<00:10,  3.18it/s]
+ 69%|   | 69/100 [00:21<00:09,  3.21it/s]
+ 70%|   | 70/100 [00:22<00:09,  3.17it/s]
+ 71%|   | 71/100 [00:22<00:09,  3.14it/s]
+ 72%|  | 72/100 [00:22<00:08,  3.16it/s]
+ 73%|  | 73/100 [00:23<00:08,  3.16it/s]
+ 74%|  | 74/100 [00:23<00:08,  3.14it/s]
+ 75%|  | 75/100 [00:23<00:07,  3.14it/s]
+ 76%|  | 76/100 [00:24<00:07,  3.13it/s]
+ 77%|  | 77/100 [00:24<00:07,  3.12it/s]
+ 78%|  | 78/100 [00:24<00:07,  3.11it/s]
+ 79%|  | 79/100 [00:24<00:06,  3.11it/s]
+ 80%|  | 80/100 [00:25<00:06,  3.12it/s]
+ 81%|  | 81/100 [00:25<00:06,  3.08it/s]
+ 82%| | 82/100 [00:25<00:05,  3.06it/s]
+ 83%| | 83/100 [00:26<00:05,  3.04it/s]
+ 84%| | 84/100 [00:26<00:05,  3.03it/s]
+ 85%| | 85/100 [00:26<00:05,  2.98it/s]
+ 86%| | 86/100 [00:27<00:04,  2.95it/s]
+ 87%| | 87/100 [00:27<00:04,  2.91it/s]
+ 88%| | 88/100 [00:28<00:04,  2.88it/s]
+ 89%| | 89/100 [00:28<00:03,  2.86it/s]
+ 90%| | 90/100 [00:28<00:03,  2.85it/s]
+ 91%| | 91/100 [00:29<00:03,  2.82it/s]
+ 92%|| 92/100 [00:29<00:02,  2.78it/s]
+ 93%|| 93/100 [00:29<00:02,  2.80it/s]
+ 94%|| 94/100 [00:30<00:02,  2.81it/s]
+ 95%|| 95/100 [00:30<00:01,  2.80it/s]
+ 96%|| 96/100 [00:30<00:01,  2.79it/s]
+ 97%|| 97/100 [00:31<00:01,  2.81it/s]
+ 98%|| 98/100 [00:31<00:00,  2.75it/s]
+ 99%|| 99/100 [00:31<00:00,  2.78it/s]
+100%|| 100/100 [00:32<00:00,  2.80it/s]
+100%|| 100/100 [00:32<00:00,  3.09it/s]
+PASSED
+tests/plugins/core/models/test_tabular_vae.py::test_vae_classification_early_stopping[standard] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:36,  2.74it/s]
+  2%|         | 2/100 [00:00<00:32,  3.00it/s]
+  3%|         | 3/100 [00:00<00:30,  3.16it/s]
+  4%|         | 4/100 [00:01<00:29,  3.23it/s]
+  5%|         | 5/100 [00:01<00:28,  3.30it/s]
+  6%|         | 6/100 [00:01<00:28,  3.29it/s]
+  7%|         | 7/100 [00:02<00:27,  3.36it/s]
+  8%|         | 8/100 [00:02<00:27,  3.34it/s]
+  9%|         | 9/100 [00:02<00:27,  3.35it/s]
+ 10%|         | 10/100 [00:03<00:26,  3.36it/s]
+ 11%|         | 11/100 [00:03<00:26,  3.37it/s]
+ 12%|        | 12/100 [00:03<00:26,  3.37it/s]
+ 13%|        | 13/100 [00:03<00:25,  3.36it/s]
+ 14%|        | 14/100 [00:04<00:25,  3.34it/s]
+ 15%|        | 15/100 [00:04<00:25,  3.37it/s]
+ 16%|        | 16/100 [00:04<00:24,  3.41it/s]
+ 17%|        | 17/100 [00:05<00:24,  3.39it/s]
+ 18%|        | 18/100 [00:05<00:24,  3.41it/s]
+ 19%|        | 19/100 [00:05<00:23,  3.41it/s]
+ 20%|        | 20/100 [00:05<00:23,  3.43it/s]
+ 21%|        | 21/100 [00:06<00:23,  3.38it/s]
+ 22%|       | 22/100 [00:06<00:23,  3.38it/s]
+ 23%|       | 23/100 [00:06<00:22,  3.38it/s]
+ 24%|       | 24/100 [00:07<00:22,  3.37it/s]
+ 25%|       | 25/100 [00:07<00:22,  3.37it/s]
+ 26%|       | 26/100 [00:07<00:22,  3.35it/s]
+ 27%|       | 27/100 [00:08<00:21,  3.34it/s]
+ 28%|       | 28/100 [00:08<00:21,  3.35it/s]
+ 29%|       | 29/100 [00:08<00:21,  3.33it/s]
+ 30%|       | 30/100 [00:08<00:20,  3.36it/s]
+ 31%|       | 31/100 [00:09<00:20,  3.33it/s]
+ 32%|      | 32/100 [00:09<00:20,  3.35it/s]
+ 33%|      | 33/100 [00:09<00:19,  3.37it/s]
+ 34%|      | 34/100 [00:10<00:19,  3.36it/s]
+ 35%|      | 35/100 [00:10<00:19,  3.36it/s]
+ 36%|      | 36/100 [00:10<00:18,  3.37it/s]
+ 37%|      | 37/100 [00:11<00:18,  3.38it/s]
+ 38%|      | 38/100 [00:11<00:18,  3.37it/s]
+ 39%|      | 39/100 [00:11<00:18,  3.36it/s]
+ 40%|      | 40/100 [00:11<00:18,  3.32it/s]
+ 41%|      | 41/100 [00:12<00:17,  3.32it/s]
+ 42%|     | 42/100 [00:12<00:17,  3.31it/s]
+ 43%|     | 43/100 [00:12<00:17,  3.33it/s]
+ 44%|     | 44/100 [00:13<00:16,  3.31it/s]
+ 45%|     | 45/100 [00:13<00:16,  3.31it/s]
+ 46%|     | 46/100 [00:13<00:16,  3.32it/s]
+ 47%|     | 47/100 [00:14<00:15,  3.32it/s]
+ 48%|     | 48/100 [00:14<00:15,  3.34it/s]
+ 49%|     | 49/100 [00:14<00:15,  3.34it/s]
+ 50%|     | 50/100 [00:14<00:15,  3.32it/s]
+ 51%|     | 51/100 [00:15<00:14,  3.31it/s]
+ 52%|    | 52/100 [00:15<00:14,  3.31it/s]
+ 53%|    | 53/100 [00:15<00:14,  3.29it/s]
+ 54%|    | 54/100 [00:16<00:13,  3.33it/s]
+ 55%|    | 55/100 [00:16<00:13,  3.33it/s]
+ 56%|    | 56/100 [00:16<00:13,  3.34it/s]
+ 57%|    | 57/100 [00:17<00:12,  3.36it/s]
+ 58%|    | 58/100 [00:17<00:12,  3.39it/s]
+ 59%|    | 59/100 [00:17<00:12,  3.38it/s]
+ 60%|    | 60/100 [00:17<00:11,  3.38it/s]
+ 61%|    | 61/100 [00:18<00:11,  3.32it/s]
+ 62%|   | 62/100 [00:18<00:11,  3.33it/s]
+ 63%|   | 63/100 [00:18<00:11,  3.31it/s]
+ 64%|   | 64/100 [00:19<00:10,  3.32it/s]
+ 65%|   | 65/100 [00:19<00:10,  3.32it/s]
+ 66%|   | 66/100 [00:19<00:10,  3.31it/s]
+ 67%|   | 67/100 [00:20<00:09,  3.32it/s]
+ 68%|   | 68/100 [00:20<00:09,  3.34it/s]
+ 69%|   | 69/100 [00:20<00:09,  3.36it/s]
+ 70%|   | 70/100 [00:20<00:08,  3.36it/s]
+ 71%|   | 71/100 [00:21<00:08,  3.34it/s]
+ 72%|  | 72/100 [00:21<00:08,  3.37it/s]
+ 73%|  | 73/100 [00:21<00:08,  3.37it/s]
+ 74%|  | 74/100 [00:22<00:07,  3.36it/s]
+ 75%|  | 75/100 [00:22<00:07,  3.34it/s]
+ 76%|  | 76/100 [00:22<00:07,  3.34it/s]
+ 77%|  | 77/100 [00:23<00:06,  3.36it/s]
+ 78%|  | 78/100 [00:23<00:06,  3.34it/s]
+ 79%|  | 79/100 [00:23<00:06,  3.34it/s]
+ 80%|  | 80/100 [00:23<00:05,  3.33it/s]
+ 81%|  | 81/100 [00:24<00:05,  3.27it/s]
+ 82%| | 82/100 [00:24<00:05,  3.26it/s]
+ 83%| | 83/100 [00:24<00:05,  3.29it/s]
+ 84%| | 84/100 [00:25<00:04,  3.31it/s]
+ 85%| | 85/100 [00:25<00:04,  3.31it/s]
+ 86%| | 86/100 [00:25<00:04,  3.31it/s]
+ 87%| | 87/100 [00:26<00:03,  3.31it/s]
+ 88%| | 88/100 [00:26<00:03,  3.34it/s]
+ 89%| | 89/100 [00:26<00:03,  3.33it/s]
+ 90%| | 90/100 [00:26<00:02,  3.34it/s]
+ 91%| | 91/100 [00:27<00:02,  3.29it/s]
+ 92%|| 92/100 [00:27<00:02,  3.31it/s]
+ 93%|| 93/100 [00:27<00:02,  3.31it/s]
+ 94%|| 94/100 [00:28<00:01,  3.31it/s]
+ 95%|| 95/100 [00:28<00:01,  3.33it/s]
+ 96%|| 96/100 [00:28<00:01,  3.36it/s]
+ 97%|| 97/100 [00:29<00:00,  3.32it/s]
+ 98%|| 98/100 [00:29<00:00,  3.37it/s]
+ 99%|| 99/100 [00:29<00:00,  3.33it/s]
+100%|| 100/100 [00:29<00:00,  3.31it/s]
+100%|| 100/100 [00:29<00:00,  3.33it/s]
+PASSED
+tests/plugins/core/models/test_tabular_vae.py::test_vae_classification_early_stopping[robust_divergence] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:35,  2.77it/s]
+  2%|         | 2/100 [00:00<00:32,  2.98it/s]
+  3%|         | 3/100 [00:01<00:32,  3.01it/s]
+  4%|         | 4/100 [00:01<00:31,  3.09it/s]
+  5%|         | 5/100 [00:01<00:30,  3.14it/s]
+  6%|         | 6/100 [00:01<00:29,  3.18it/s]
+  7%|         | 7/100 [00:02<00:29,  3.18it/s]
+  8%|         | 8/100 [00:02<00:29,  3.17it/s]
+  9%|         | 9/100 [00:02<00:28,  3.18it/s]
+ 10%|         | 10/100 [00:03<00:28,  3.13it/s]
+ 11%|         | 11/100 [00:03<00:28,  3.12it/s]
+ 12%|        | 12/100 [00:03<00:27,  3.15it/s]
+ 13%|        | 13/100 [00:04<00:27,  3.16it/s]
+ 14%|        | 14/100 [00:04<00:27,  3.16it/s]
+ 15%|        | 15/100 [00:04<00:26,  3.16it/s]
+ 16%|        | 16/100 [00:05<00:26,  3.17it/s]
+ 17%|        | 17/100 [00:05<00:26,  3.18it/s]
+ 18%|        | 18/100 [00:05<00:25,  3.20it/s]
+ 19%|        | 19/100 [00:06<00:25,  3.15it/s]
+ 20%|        | 20/100 [00:06<00:25,  3.17it/s]
+ 21%|        | 21/100 [00:06<00:25,  3.16it/s]
+ 22%|       | 22/100 [00:06<00:24,  3.16it/s]
+ 23%|       | 23/100 [00:07<00:24,  3.16it/s]
+ 24%|       | 24/100 [00:07<00:24,  3.16it/s]
+ 25%|       | 25/100 [00:07<00:23,  3.20it/s]
+ 26%|       | 26/100 [00:08<00:23,  3.18it/s]
+ 27%|       | 27/100 [00:08<00:22,  3.18it/s]
+ 28%|       | 28/100 [00:08<00:22,  3.22it/s]
+ 29%|       | 29/100 [00:09<00:22,  3.19it/s]
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+PASSED
+tests/plugins/core/models/test_ts_gan.py::test_ts_gan_conditional_static_data[GoogleStocksDataloader] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  4.71it/s]
+ 20%|        | 2/10 [00:00<00:01,  4.58it/s]
+ 30%|       | 3/10 [00:00<00:01,  4.59it/s]
+ 40%|      | 4/10 [00:00<00:01,  4.56it/s]
+ 50%|     | 5/10 [00:01<00:01,  4.56it/s]
+ 60%|    | 6/10 [00:01<00:00,  4.57it/s]
+ 70%|   | 7/10 [00:01<00:00,  4.61it/s]
+ 80%|  | 8/10 [00:01<00:00,  4.60it/s]
+ 90%| | 9/10 [00:01<00:00,  4.60it/s]
+100%|| 10/10 [00:02<00:00,  4.61it/s]
+100%|| 10/10 [00:02<00:00,  4.60it/s]
+PASSED
+tests/plugins/core/models/test_ts_gan.py::test_ts_gan_conditional_observation_times[SineDataloader] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:03,  2.38it/s]
+ 20%|        | 2/10 [00:00<00:03,  2.35it/s]
+ 30%|       | 3/10 [00:01<00:02,  2.37it/s]
+ 40%|      | 4/10 [00:01<00:02,  2.35it/s]
+ 50%|     | 5/10 [00:02<00:02,  2.37it/s]
+ 60%|    | 6/10 [00:02<00:01,  2.37it/s]
+ 70%|   | 7/10 [00:02<00:01,  2.34it/s]
+ 80%|  | 8/10 [00:03<00:00,  2.35it/s]
+ 90%| | 9/10 [00:03<00:00,  2.34it/s]
+100%|| 10/10 [00:04<00:00,  2.34it/s]
+100%|| 10/10 [00:04<00:00,  2.35it/s]
+PASSED
+tests/plugins/core/models/test_ts_gan.py::test_ts_gan_conditional_observation_times[GoogleStocksDataloader] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  4.60it/s]
+ 20%|        | 2/10 [00:00<00:01,  4.62it/s]
+ 30%|       | 3/10 [00:00<00:01,  4.64it/s]
+ 40%|      | 4/10 [00:00<00:01,  4.39it/s]
+ 50%|     | 5/10 [00:01<00:01,  4.41it/s]
+ 60%|    | 6/10 [00:01<00:00,  4.46it/s]
+ 70%|   | 7/10 [00:01<00:00,  4.49it/s]
+ 80%|  | 8/10 [00:01<00:00,  4.51it/s]
+ 90%| | 9/10 [00:01<00:00,  4.52it/s]
+100%|| 10/10 [00:02<00:00,  4.54it/s]
+100%|| 10/10 [00:02<00:00,  4.51it/s]
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-LSTM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-GRU] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-RNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-Transformer] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-MLSTM_FCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-TCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-InceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-InceptionTimePlus] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-XceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-ResCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-OmniScaleCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[classification-XCM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-LSTM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-GRU] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-RNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-Transformer] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-MLSTM_FCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-TCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-InceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-InceptionTimePlus] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-XceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-ResCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-OmniScaleCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_sanity[regression-XCM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-LSTM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-GRU] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-RNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-Transformer] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-MLSTM_FCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-TCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-InceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-InceptionTimePlus] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-XceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-ResCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-OmniScaleCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-SineDataloader-XCM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-LSTM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-GRU] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-RNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-Transformer] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-MLSTM_FCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-TCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-InceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-InceptionTimePlus] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-XceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-ResCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-OmniScaleCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[True-GoogleStocksDataloader-XCM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-LSTM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-GRU] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-RNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-Transformer] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-MLSTM_FCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-TCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-InceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-InceptionTimePlus] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-XceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-ResCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-OmniScaleCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-SineDataloader-XCM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-LSTM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-GRU] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-RNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-Transformer] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-MLSTM_FCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-TCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-InceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-InceptionTimePlus] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-XceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-ResCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-OmniScaleCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_regression_fit_predict[False-GoogleStocksDataloader-XCM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-LSTM] LSTM 0.86
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-GRU] GRU 0.8
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-RNN] RNN 0.775
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-Transformer] Transformer 0.95
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-MLSTM_FCN] MLSTM_FCN 0.785
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-TCN] TCN 0.82
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-InceptionTime] InceptionTime 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-InceptionTimePlus] InceptionTimePlus 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-XceptionTime] XceptionTime 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-ResCNN] ResCNN 0.97
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-OmniScaleCNN] OmniScaleCNN 0.505
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[SineDataloader-XCM] XCM 0.99
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-LSTM] LSTM 0.5
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-GRU] GRU 0.67
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-RNN] RNN 0.81
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-Transformer] Transformer 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-MLSTM_FCN] MLSTM_FCN 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-TCN] TCN 0.5
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-InceptionTime] InceptionTime 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-InceptionTimePlus] InceptionTimePlus 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-XceptionTime] XceptionTime 0.98
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-ResCNN] ResCNN 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-OmniScaleCNN] OmniScaleCNN 0.89
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_classification_fit_predict[GoogleStocksDataloader-XCM] XCM 1.0
+PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[LSTM] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[GRU] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[RNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[Transformer] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[MLSTM_FCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[TCN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[InceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[InceptionTimePlus] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[XceptionTime] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[ResCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[OmniScaleCNN] PASSED
+tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[XCM] PASSED
+tests/plugins/core/models/test_ts_tabular_gan.py::test_network_config PASSED
+tests/plugins/core/models/test_ts_tabular_gan.py::test_ts_tabular_gan_conditional_static_data[SineDataloader] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.14s/it]
+ 20%|        | 2/10 [00:02<00:08,  1.12s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.12s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.11s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.12s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.12s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.12s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.11s/it]
+ 90%| | 9/10 [00:10<00:01,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+PASSED
+tests/plugins/core/models/test_ts_tabular_gan.py::test_ts_tabular_gan_conditional_static_data[GoogleStocksDataloader] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:05,  1.64it/s]
+ 20%|        | 2/10 [00:01<00:04,  1.68it/s]
+ 30%|       | 3/10 [00:01<00:04,  1.71it/s]
+ 40%|      | 4/10 [00:02<00:03,  1.73it/s]
+ 50%|     | 5/10 [00:02<00:02,  1.75it/s]
+ 60%|    | 6/10 [00:03<00:02,  1.74it/s]
+ 70%|   | 7/10 [00:04<00:01,  1.76it/s]
+ 80%|  | 8/10 [00:04<00:01,  1.76it/s]
+ 90%| | 9/10 [00:05<00:00,  1.77it/s]
+100%|| 10/10 [00:05<00:00,  1.77it/s]
+100%|| 10/10 [00:05<00:00,  1.75it/s]
+PASSED
+tests/plugins/core/models/test_ts_tabular_gan.py::test_ts_tabular_gan_conditional_observation_times[SineDataloader] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.15s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.13s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.14s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.14s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.12s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.12s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.11s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.11s/it]
+ 90%| | 9/10 [00:10<00:01,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+PASSED
+tests/plugins/core/models/test_ts_tabular_gan.py::test_ts_tabular_gan_conditional_observation_times[GoogleStocksDataloader] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:05,  1.68it/s]
+ 20%|        | 2/10 [00:01<00:04,  1.72it/s]
+ 30%|       | 3/10 [00:01<00:04,  1.74it/s]
+ 40%|      | 4/10 [00:02<00:03,  1.74it/s]
+ 50%|     | 5/10 [00:02<00:02,  1.76it/s]
+ 60%|    | 6/10 [00:03<00:02,  1.76it/s]
+ 70%|   | 7/10 [00:04<00:01,  1.76it/s]
+ 80%|  | 8/10 [00:04<00:01,  1.75it/s]
+ 90%| | 9/10 [00:05<00:00,  1.74it/s]
+100%|| 10/10 [00:05<00:00,  1.75it/s]
+100%|| 10/10 [00:05<00:00,  1.75it/s]
+PASSED
+tests/plugins/core/models/test_ts_tabular_vae.py::test_network_config PASSED
+tests/plugins/core/models/test_ts_tabular_vae.py::test_ts_vae_generation[SineDataloader] PASSED
+tests/plugins/core/models/test_ts_tabular_vae.py::test_ts_vae_generation[GoogleStocksDataloader] PASSED
+tests/plugins/core/models/test_vae.py::test_network_config PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0-relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0-elu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0-leaky_relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0.5-relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0.5-elu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0.5-leaky_relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0.2-relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0.2-elu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-True-0.2-leaky_relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-False-0-relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-False-0-elu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-False-0-leaky_relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-False-0.5-relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.001-False-0.5-elu] PASSED
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+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.0003-False-0-relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.0003-False-0-elu] PASSED
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+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.0003-False-0.5-relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.0003-False-0.5-elu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.0003-False-0.5-leaky_relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[2-0.0003-False-0.2-relu] PASSED
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+tests/plugins/core/models/test_vae.py::test_basic_network[3-0.0003-True-0-leaky_relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[3-0.0003-True-0.5-relu] PASSED
+tests/plugins/core/models/test_vae.py::test_basic_network[3-0.0003-True-0.5-elu] PASSED
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+tests/plugins/core/models/test_vae.py::test_basic_network[3-0.0003-False-0.2-leaky_relu] PASSED
+tests/plugins/core/models/test_vae.py::test_vae_classification[standard] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  5.05it/s]
+ 20%|        | 2/10 [00:00<00:01,  5.27it/s]
+ 30%|       | 3/10 [00:00<00:01,  5.30it/s]
+ 40%|      | 4/10 [00:00<00:01,  5.35it/s]
+ 50%|     | 5/10 [00:00<00:00,  5.38it/s]
+ 60%|    | 6/10 [00:01<00:00,  5.39it/s]
+ 70%|   | 7/10 [00:01<00:00,  5.46it/s]
+ 80%|  | 8/10 [00:01<00:00,  5.46it/s]
+ 90%| | 9/10 [00:01<00:00,  5.41it/s]
+100%|| 10/10 [00:01<00:00,  5.35it/s]
+100%|| 10/10 [00:01<00:00,  5.36it/s]
+PASSED
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+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  5.17it/s]
+ 20%|        | 2/10 [00:00<00:01,  5.29it/s]
+ 30%|       | 3/10 [00:00<00:01,  5.25it/s]
+ 40%|      | 4/10 [00:00<00:01,  5.28it/s]
+ 50%|     | 5/10 [00:00<00:00,  5.34it/s]
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+ 70%|   | 7/10 [00:01<00:00,  5.31it/s]
+ 80%|  | 8/10 [00:01<00:00,  5.33it/s]
+ 90%| | 9/10 [00:01<00:00,  5.37it/s]
+100%|| 10/10 [00:01<00:00,  5.30it/s]
+100%|| 10/10 [00:01<00:00,  5.31it/s]
+PASSED
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+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  4.70it/s]
+ 20%|        | 2/10 [00:00<00:01,  5.05it/s]
+ 30%|       | 3/10 [00:00<00:01,  5.20it/s]
+ 40%|      | 4/10 [00:00<00:01,  5.20it/s]
+ 50%|     | 5/10 [00:00<00:00,  5.26it/s]
+ 60%|    | 6/10 [00:01<00:00,  5.27it/s]
+ 70%|   | 7/10 [00:01<00:00,  5.19it/s]
+ 80%|  | 8/10 [00:01<00:00,  5.18it/s]
+ 90%| | 9/10 [00:01<00:00,  5.19it/s]
+100%|| 10/10 [00:01<00:00,  5.15it/s]
+100%|| 10/10 [00:01<00:00,  5.17it/s]
+PASSED
+tests/plugins/core/models/dag/test_dag_sanity.py::test_sanity 
+Training: |          | 0/? [00:00<?, ?it/s]
+Training:   0%|          | 0/1 [00:00<?, ?it/s]
+Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s] 
+Epoch 0: 100%|| 1/1 [00:03<00:00,  0.29it/s]
+Epoch 0: 100%|| 1/1 [00:03<00:00,  0.29it/s, v_num=2.01e+7, mse_loss=0.723, dsl_loss=3.660, total_loss=4.380]
+Epoch 0: 100%|| 1/1 [00:03<00:00,  0.29it/s, v_num=2.01e+7, mse_loss=0.723, dsl_loss=3.660, total_loss=4.380]
+Epoch 0: 100%|| 1/1 [00:03<00:00,  0.29it/s, v_num=2.01e+7, mse_loss=0.723, dsl_loss=3.660, total_loss=4.380]
+XFAIL
+tests/plugins/core/models/survival_analysis/test_surv_aft.py::test_sanity PASSED
+tests/plugins/core/models/survival_analysis/test_surv_aft.py::test_hyperparams PASSED
+tests/plugins/core/models/survival_analysis/test_surv_aft.py::test_train_prediction PASSED
+tests/plugins/core/models/survival_analysis/test_surv_coxph.py::test_sanity PASSED
+tests/plugins/core/models/survival_analysis/test_surv_coxph.py::test_hyperparams PASSED
+tests/plugins/core/models/survival_analysis/test_surv_coxph.py::test_train_prediction PASSED
+tests/plugins/core/models/survival_analysis/test_surv_deephit.py::test_sanity PASSED
+tests/plugins/core/models/survival_analysis/test_surv_deephit.py::test_hyperparams PASSED
+tests/plugins/core/models/survival_analysis/test_surv_deephit.py::test_train_prediction PASSED
+tests/plugins/core/models/survival_analysis/test_surv_metrics.py::test_km_surv_function PASSED
+tests/plugins/core/models/survival_analysis/test_surv_metrics.py::test_nonparametric_distance PASSED
+tests/plugins/core/models/survival_analysis/test_surv_xgb.py::test_sanity PASSED
+tests/plugins/core/models/survival_analysis/test_surv_xgb.py::test_hyperparams PASSED
+tests/plugins/core/models/survival_analysis/test_surv_xgb.py::test_train_prediction FAILED
+tests/plugins/core/models/time_series_survival/test_ts_surv_coxph.py::test_sanity PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_coxph.py::test_hyperparams PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_coxph.py::test_train_prediction_coxph[MLP-LSTM] Perf ts_coxph {'c_index': '0.8344 +/- 0.0146', 'brier_score': '0.109 +/- 0.019'}
+PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_coxph.py::test_train_prediction_coxph[MLP-Transformer] Perf ts_coxph {'c_index': '0.8367 +/- 0.0099', 'brier_score': '0.1132 +/- 0.0244'}
+PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_dynamic_deephit.py::test_sanity PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_dynamic_deephit.py::test_hyperparams PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_dynamic_deephit.py::test_train[GRU] PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_dynamic_deephit.py::test_train[LSTM] PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_dynamic_deephit.py::test_train[RNN] PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_dynamic_deephit.py::test_train[Transformer] PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_dynamic_deephit.py::test_train_prediction_dyn_deephit[MLP-LSTM] Perf dynamic_deephit {'c_index': '0.8228 +/- 0.0082', 'brier_score': '0.1142 +/- 0.0071'}
+PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_dynamic_deephit.py::test_train_prediction_dyn_deephit[MLP-Transformer] Perf dynamic_deephit {'c_index': '0.8216 +/- 0.0106', 'brier_score': '0.1153 +/- 0.0169'}
+PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_xgb.py::test_sanity PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_xgb.py::test_hyperparams PASSED
+tests/plugins/core/models/time_series_survival/test_ts_surv_xgb.py::test_train_prediction[Transformer] FAILED
+tests/plugins/core/models/time_to_event/test_tte_aft.py::test_sanity PASSED
+tests/plugins/core/models/time_to_event/test_tte_aft.py::test_hyperparams PASSED
+tests/plugins/core/models/time_to_event/test_tte_aft.py::test_train_prediction PASSED
+tests/plugins/core/models/time_to_event/test_tte_coxph.py::test_sanity PASSED
+tests/plugins/core/models/time_to_event/test_tte_coxph.py::test_hyperparams PASSED
+tests/plugins/core/models/time_to_event/test_tte_coxph.py::test_train_prediction PASSED
+tests/plugins/core/models/time_to_event/test_tte_date.py::test_sanity PASSED
+tests/plugins/core/models/time_to_event/test_tte_date.py::test_hyperparams PASSED
+tests/plugins/core/models/time_to_event/test_tte_date.py::test_train_prediction PASSED
+tests/plugins/core/models/time_to_event/test_tte_deephit.py::test_sanity PASSED
+tests/plugins/core/models/time_to_event/test_tte_deephit.py::test_hyperparams PASSED
+tests/plugins/core/models/time_to_event/test_tte_deephit.py::test_train_prediction PASSED
+tests/plugins/core/models/time_to_event/test_tte_metrics.py::test_expected_time_error[l1-results0] PASSED
+tests/plugins/core/models/time_to_event/test_tte_metrics.py::test_expected_time_error[l2-results1] PASSED
+tests/plugins/core/models/time_to_event/test_tte_metrics.py::test_ranking_error PASSED
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+tests/plugins/core/models/time_to_event/test_tte_survival_function_regression.py::test_sanity PASSED
+tests/plugins/core/models/time_to_event/test_tte_survival_function_regression.py::test_hyperparams PASSED
+tests/plugins/core/models/time_to_event/test_tte_survival_function_regression.py::test_train_prediction PASSED
+tests/plugins/core/models/time_to_event/test_tte_survival_time_series.py::test_sanity PASSED
+tests/plugins/core/models/time_to_event/test_tte_survival_time_series.py::test_hyperparams PASSED
+tests/plugins/core/models/time_to_event/test_tte_survival_time_series.py::test_train_prediction[RNN] SKIPPED
+tests/plugins/core/models/time_to_event/test_tte_survival_time_series.py::test_train_prediction[Transformer] SKIPPED
+tests/plugins/core/models/time_to_event/test_tte_survival_time_series.py::test_train_prediction[LSTM] SKIPPED
+tests/plugins/core/models/time_to_event/test_tte_survival_time_series.py::test_train_prediction[GRU] SKIPPED
+tests/plugins/core/models/time_to_event/test_tte_tenn.py::test_sanity PASSED
+tests/plugins/core/models/time_to_event/test_tte_tenn.py::test_hyperparams PASSED
+tests/plugins/core/models/time_to_event/test_tte_tenn.py::test_train_prediction PASSED
+tests/plugins/core/models/time_to_event/test_tte_xgb.py::test_sanity PASSED
+tests/plugins/core/models/time_to_event/test_tte_xgb.py::test_hyperparams PASSED
+tests/plugins/core/models/time_to_event/test_tte_xgb.py::test_train_prediction FAILED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_fit[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:09,  1.08s/it]
+ 20%|        | 2/10 [00:02<00:08,  1.09s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.09s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.08s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.07s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.07s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.05s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.06s/it]
+ 90%| | 9/10 [00:09<00:01,  1.06s/it]
+100%|| 10/10 [00:10<00:00,  1.07s/it]
+100%|| 10/10 [00:10<00:00,  1.07s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_fit[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.14s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.13s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.12s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.11s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.10s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.10s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.10s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.10s/it]
+ 90%| | 9/10 [00:09<00:01,  1.10s/it]
+100%|| 10/10 [00:11<00:00,  1.10s/it]
+100%|| 10/10 [00:11<00:00,  1.11s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_fit[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.13s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.13s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.12s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.11s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.12s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.11s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.11s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.11s/it]
+ 90%| | 9/10 [00:10<00:01,  1.12s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate[True-test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.17s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.16s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.14s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.13s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.12s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.11s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.11s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.11s/it]
+ 90%| | 9/10 [00:10<00:01,  1.10s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate[True-test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.18s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.14s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.14s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.14s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.14s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.13s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.13s/it]
+ 80%|  | 8/10 [00:09<00:02,  1.12s/it]
+ 90%| | 9/10 [00:10<00:01,  1.12s/it]
+100%|| 10/10 [00:11<00:00,  1.10s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate[True-test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.15s/it]
+ 20%|        | 2/10 [00:02<00:08,  1.12s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.12s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.12s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.12s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.14s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.14s/it]
+ 80%|  | 8/10 [00:09<00:02,  1.12s/it]
+ 90%| | 9/10 [00:10<00:01,  1.12s/it]
+100%|| 10/10 [00:11<00:00,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate[False-test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.17s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.13s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.13s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.11s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.11s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.11s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.10s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.11s/it]
+ 90%| | 9/10 [00:10<00:01,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate[False-test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.17s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.16s/it]
+ 30%|       | 3/10 [00:03<00:08,  1.14s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.13s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.13s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.13s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.13s/it]
+ 80%|  | 8/10 [00:09<00:02,  1.13s/it]
+ 90%| | 9/10 [00:10<00:01,  1.13s/it]
+100%|| 10/10 [00:11<00:00,  1.13s/it]
+100%|| 10/10 [00:11<00:00,  1.13s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate[False-test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.17s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.14s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.13s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.11s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.11s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.12s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.12s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.11s/it]
+ 90%| | 9/10 [00:10<00:01,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate_domain[target_domains0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.16s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.13s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.13s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.15s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.13s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.13s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.13s/it]
+ 80%|  | 8/10 [00:09<00:02,  1.13s/it]
+ 90%| | 9/10 [00:10<00:01,  1.13s/it]
+100%|| 10/10 [00:11<00:00,  1.13s/it]
+100%|| 10/10 [00:11<00:00,  1.13s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate_domain[target_domains1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:10,  1.12s/it]
+ 20%|        | 2/10 [00:02<00:09,  1.13s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.11s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.12s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.11s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.11s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.11s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.11s/it]
+ 90%| | 9/10 [00:09<00:01,  1.11s/it]
+100%|| 10/10 [00:11<00:00,  1.10s/it]
+100%|| 10/10 [00:11<00:00,  1.11s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate_domain[target_domains2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:01<00:09,  1.09s/it]
+ 20%|        | 2/10 [00:02<00:08,  1.08s/it]
+ 30%|       | 3/10 [00:03<00:07,  1.10s/it]
+ 40%|      | 4/10 [00:04<00:06,  1.10s/it]
+ 50%|     | 5/10 [00:05<00:05,  1.10s/it]
+ 60%|    | 6/10 [00:06<00:04,  1.11s/it]
+ 70%|   | 7/10 [00:07<00:03,  1.11s/it]
+ 80%|  | 8/10 [00:08<00:02,  1.12s/it]
+ 90%| | 9/10 [00:10<00:01,  1.12s/it]
+100%|| 10/10 [00:11<00:00,  1.12s/it]
+100%|| 10/10 [00:11<00:00,  1.11s/it]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate_constraints[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  7.86it/s]
+ 20%|        | 2/10 [00:00<00:00,  8.56it/s]
+ 30%|       | 3/10 [00:00<00:00,  8.89it/s]
+ 40%|      | 4/10 [00:00<00:00,  8.97it/s]
+ 50%|     | 5/10 [00:00<00:00,  8.83it/s]
+ 60%|    | 6/10 [00:00<00:00,  8.99it/s]
+ 80%|  | 8/10 [00:00<00:00,  9.30it/s]
+ 90%| | 9/10 [00:00<00:00,  9.38it/s]
+100%|| 10/10 [00:01<00:00,  9.31it/s]
+100%|| 10/10 [00:01<00:00,  9.10it/s]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate_constraints[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  6.89it/s]
+ 20%|        | 2/10 [00:00<00:01,  7.79it/s]
+ 30%|       | 3/10 [00:00<00:00,  8.34it/s]
+ 40%|      | 4/10 [00:00<00:00,  8.25it/s]
+ 50%|     | 5/10 [00:00<00:00,  8.39it/s]
+ 60%|    | 6/10 [00:00<00:00,  8.64it/s]
+ 70%|   | 7/10 [00:00<00:00,  8.84it/s]
+ 80%|  | 8/10 [00:00<00:00,  9.05it/s]
+ 90%| | 9/10 [00:01<00:00,  9.16it/s]
+100%|| 10/10 [00:01<00:00,  8.94it/s]
+100%|| 10/10 [00:01<00:00,  8.65it/s]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_plugin_generate_constraints[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  7.99it/s]
+ 20%|        | 2/10 [00:00<00:00,  8.93it/s]
+ 30%|       | 3/10 [00:00<00:00,  9.01it/s]
+ 40%|      | 4/10 [00:00<00:00,  9.22it/s]
+ 50%|     | 5/10 [00:00<00:00,  9.38it/s]
+ 60%|    | 6/10 [00:00<00:00,  9.25it/s]
+ 70%|   | 7/10 [00:00<00:00,  9.39it/s]
+ 90%| | 9/10 [00:00<00:00,  9.40it/s]
+100%|| 10/10 [00:01<00:00,  9.41it/s]
+100%|| 10/10 [00:01<00:00,  9.27it/s]
+PASSED
+tests/plugins/domain_adaptation/test_radialgan.py::test_sample_hyperparams PASSED
+tests/plugins/generic/test_arf.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/generic/test_arf.py::test_plugin_fit[test_plugin0] Initial accuracy is 0.8655126498002663
+Iteration number 1 reached accuracy of 0.5043275632490013.
+Iteration number 2 reached accuracy of 0.49900133155792276.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_fit[test_plugin1] Initial accuracy is 0.8655126498002663
+Iteration number 1 reached accuracy of 0.5043275632490013.
+Iteration number 2 reached accuracy of 0.49900133155792276.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_fit[test_plugin2] Initial accuracy is 0.8655126498002663
+Iteration number 1 reached accuracy of 0.5043275632490013.
+Iteration number 2 reached accuracy of 0.49900133155792276.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate[True-test_plugin0] Initial accuracy is 0.6787330316742082
+Iteration number 1 reached accuracy of 0.4841628959276018.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate[True-test_plugin1] Initial accuracy is 0.6787330316742082
+Iteration number 1 reached accuracy of 0.4841628959276018.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate[True-test_plugin2] Initial accuracy is 0.6787330316742082
+Iteration number 1 reached accuracy of 0.4841628959276018.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate[False-test_plugin0] Initial accuracy is 0.6787330316742082
+Iteration number 1 reached accuracy of 0.4841628959276018.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate[False-test_plugin1] Initial accuracy is 0.6787330316742082
+Iteration number 1 reached accuracy of 0.4841628959276018.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate[False-test_plugin2] Initial accuracy is 0.6787330316742082
+Iteration number 1 reached accuracy of 0.4841628959276018.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate_constraints_arf[test_plugin0] Initial accuracy is 0.8366666666666667
+Iteration number 1 reached accuracy of 0.43666666666666665.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate_constraints_arf[test_plugin1] Initial accuracy is 0.8366666666666667
+Iteration number 1 reached accuracy of 0.43666666666666665.
+PASSED
+tests/plugins/generic/test_arf.py::test_plugin_generate_constraints_arf[test_plugin2] Initial accuracy is 0.8366666666666667
+Iteration number 1 reached accuracy of 0.43666666666666665.
+PASSED
+tests/plugins/generic/test_arf.py::test_sample_hyperparams PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[k2-hillclimb] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[k2-mmhc] 
+  0%|          | 0/1000000 [00:00<?, ?it/s]
+  0%|          | 0/1000000 [00:00<?, ?it/s]
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[k2-pc] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[k2-tree_search] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[k2-exhaustive] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bdeu-hillclimb] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bdeu-mmhc] 
+  0%|          | 0/1000000 [00:00<?, ?it/s]
+  0%|          | 0/1000000 [00:00<?, ?it/s]
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bdeu-pc] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bdeu-tree_search] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bdeu-exhaustive] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bic-hillclimb] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bic-mmhc] 
+  0%|          | 0/1000000 [00:00<?, ?it/s]
+  0%|          | 0/1000000 [00:00<?, ?it/s]
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bic-pc] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bic-tree_search] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bic-exhaustive] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bds-hillclimb] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bds-mmhc] 
+  0%|          | 0/1000000 [00:00<?, ?it/s]
+  0%|          | 0/1000000 [00:00<?, ?it/s]
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bds-pc] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bds-tree_search] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_fit[bds-exhaustive] PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_generate[test_plugin0] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_generate[test_plugin1] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_generate[test_plugin2] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_generate_constraints[test_plugin0] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_generate_constraints[test_plugin1] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_bayesian_network.py::test_plugin_generate_constraints[test_plugin2] /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_fit[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  3.23it/s]
+ 20%|        | 2/10 [00:00<00:02,  3.49it/s]
+ 30%|       | 3/10 [00:00<00:01,  3.54it/s]
+ 40%|      | 4/10 [00:01<00:01,  3.61it/s]
+ 50%|     | 5/10 [00:01<00:01,  3.62it/s]
+ 60%|    | 6/10 [00:01<00:01,  3.63it/s]
+ 70%|   | 7/10 [00:01<00:00,  3.66it/s]
+ 80%|  | 8/10 [00:02<00:00,  3.61it/s]
+ 90%| | 9/10 [00:02<00:00,  3.60it/s]
+100%|| 10/10 [00:02<00:00,  3.60it/s]
+100%|| 10/10 [00:02<00:00,  3.59it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_fit[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  3.11it/s]
+ 20%|        | 2/10 [00:00<00:02,  3.33it/s]
+ 30%|       | 3/10 [00:00<00:02,  3.46it/s]
+ 40%|      | 4/10 [00:01<00:01,  3.50it/s]
+ 50%|     | 5/10 [00:01<00:01,  3.45it/s]
+ 60%|    | 6/10 [00:01<00:01,  3.47it/s]
+ 70%|   | 7/10 [00:02<00:00,  3.54it/s]
+ 80%|  | 8/10 [00:02<00:00,  3.50it/s]
+ 90%| | 9/10 [00:02<00:00,  3.52it/s]
+100%|| 10/10 [00:02<00:00,  3.51it/s]
+100%|| 10/10 [00:02<00:00,  3.48it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_fit[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  3.25it/s]
+ 20%|        | 2/10 [00:00<00:02,  3.44it/s]
+ 30%|       | 3/10 [00:00<00:01,  3.51it/s]
+ 40%|      | 4/10 [00:01<00:01,  3.55it/s]
+ 50%|     | 5/10 [00:01<00:01,  3.55it/s]
+ 60%|    | 6/10 [00:01<00:01,  3.61it/s]
+ 70%|   | 7/10 [00:01<00:00,  3.65it/s]
+ 80%|  | 8/10 [00:02<00:00,  3.61it/s]
+ 90%| | 9/10 [00:02<00:00,  3.62it/s]
+100%|| 10/10 [00:02<00:00,  3.61it/s]
+100%|| 10/10 [00:02<00:00,  3.58it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate[True-test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  3.15it/s]
+ 20%|        | 2/10 [00:00<00:02,  3.47it/s]
+ 30%|       | 3/10 [00:00<00:01,  3.54it/s]
+ 40%|      | 4/10 [00:01<00:01,  3.58it/s]
+ 50%|     | 5/10 [00:01<00:01,  3.57it/s]
+ 60%|    | 6/10 [00:01<00:01,  3.59it/s]
+ 70%|   | 7/10 [00:01<00:00,  3.57it/s]
+ 80%|  | 8/10 [00:02<00:00,  3.63it/s]
+ 90%| | 9/10 [00:02<00:00,  3.58it/s]
+100%|| 10/10 [00:02<00:00,  3.57it/s]
+100%|| 10/10 [00:02<00:00,  3.56it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate[True-test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  3.20it/s]
+ 20%|        | 2/10 [00:00<00:02,  3.47it/s]
+ 30%|       | 3/10 [00:00<00:01,  3.53it/s]
+ 40%|      | 4/10 [00:01<00:02,  2.21it/s]
+ 50%|     | 5/10 [00:01<00:01,  2.54it/s]
+ 60%|    | 6/10 [00:02<00:01,  2.82it/s]
+ 70%|   | 7/10 [00:02<00:01,  2.93it/s]
+ 80%|  | 8/10 [00:02<00:00,  3.11it/s]
+ 90%| | 9/10 [00:03<00:00,  3.28it/s]
+100%|| 10/10 [00:03<00:00,  3.41it/s]
+100%|| 10/10 [00:03<00:00,  3.06it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate[True-test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  3.19it/s]
+ 20%|        | 2/10 [00:00<00:02,  3.39it/s]
+ 30%|       | 3/10 [00:00<00:02,  3.43it/s]
+ 40%|      | 4/10 [00:01<00:01,  3.40it/s]
+ 50%|     | 5/10 [00:01<00:01,  3.46it/s]
+ 60%|    | 6/10 [00:01<00:01,  3.52it/s]
+ 70%|   | 7/10 [00:02<00:00,  3.53it/s]
+ 80%|  | 8/10 [00:02<00:00,  3.62it/s]
+ 90%| | 9/10 [00:02<00:00,  3.63it/s]
+100%|| 10/10 [00:02<00:00,  3.59it/s]
+100%|| 10/10 [00:02<00:00,  3.52it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate[False-test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  3.28it/s]
+ 20%|        | 2/10 [00:00<00:02,  3.57it/s]
+ 30%|       | 3/10 [00:00<00:01,  3.63it/s]
+ 40%|      | 4/10 [00:01<00:01,  3.59it/s]
+ 50%|     | 5/10 [00:01<00:01,  3.60it/s]
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+100%|| 10/10 [00:02<00:00,  3.52it/s]
+100%|| 10/10 [00:02<00:00,  3.55it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate[False-test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
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+100%|| 10/10 [00:02<00:00,  3.62it/s]
+100%|| 10/10 [00:02<00:00,  3.56it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate[False-test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  3.24it/s]
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+100%|| 10/10 [00:02<00:00,  3.66it/s]
+100%|| 10/10 [00:02<00:00,  3.55it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate_constraints_ctgan[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 30%|       | 3/10 [00:00<00:00, 23.35it/s]
+ 70%|   | 7/10 [00:00<00:00, 28.46it/s]
+100%|| 10/10 [00:00<00:00, 28.88it/s]
+100%|| 10/10 [00:00<00:00, 28.18it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate_constraints_ctgan[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 30%|       | 3/10 [00:00<00:00, 22.92it/s]
+ 60%|    | 6/10 [00:00<00:00, 26.56it/s]
+100%|| 10/10 [00:00<00:00, 28.59it/s]
+100%|| 10/10 [00:00<00:00, 27.63it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_generate_constraints_ctgan[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 30%|       | 3/10 [00:00<00:00, 23.45it/s]
+ 70%|   | 7/10 [00:00<00:00, 28.16it/s]
+100%|| 10/10 [00:00<00:00, 28.72it/s]
+PASSED
+tests/plugins/generic/test_ctgan.py::test_sample_hyperparams PASSED
+tests/plugins/generic/test_ctgan.py::test_plugin_conditional_ctgan 
+  0%|          | 0/2000 [00:00<?, ?it/s]
+  0%|          | 3/2000 [00:00<01:24, 23.69it/s]
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+ 40%|      | 802/2000 [00:39<01:54, 10.42it/s]
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+ 42%|     | 848/2000 [00:41<00:38, 30.14it/s]
+ 42%|     | 849/2000 [00:42<00:56, 20.21it/s]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_fit[test_plugin0] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.942]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.905]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.847]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.794]
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+Epoch:   1%|         | 13/1000 [00:00<00:15, 64.61it/s, loss=0.592]
+Epoch:   1%|         | 13/1000 [00:00<00:15, 64.61it/s, loss=0.581]
+Epoch:   1%|         | 13/1000 [00:00<00:15, 64.61it/s, loss=0.573]
+Epoch:   1%|         | 13/1000 [00:00<00:15, 64.61it/s, loss=0.564]
+Epoch:   1%|         | 13/1000 [00:00<00:15, 64.61it/s, loss=0.557]
+Epoch:   2%|         | 21/1000 [00:00<00:13, 70.11it/s, loss=0.557]
+Epoch:   2%|         | 21/1000 [00:00<00:13, 70.11it/s, loss=0.551]
+Epoch:   2%|         | 21/1000 [00:00<00:13, 70.11it/s, loss=0.546]
+Epoch:   2%|         | 21/1000 [00:00<00:13, 70.11it/s, loss=0.541]
+Epoch:   2%|         | 21/1000 [00:00<00:13, 70.11it/s, loss=0.536]
+Epoch:   2%|         | 21/1000 [00:00<00:13, 70.11it/s, loss=0.53] 
+Epoch:   2%|         | 21/1000 [00:00<00:13, 70.11it/s, loss=0.524]
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+Epoch:   2%|         | 21/1000 [00:00<00:13, 70.11it/s, loss=0.515]
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+Epoch:   3%|         | 30/1000 [00:00<00:12, 76.55it/s, loss=0.504]
+Epoch:   3%|         | 30/1000 [00:00<00:12, 76.55it/s, loss=0.501]
+Epoch:   3%|         | 30/1000 [00:00<00:12, 76.55it/s, loss=0.498]
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+Epoch:   4%|         | 40/1000 [00:00<00:11, 81.53it/s, loss=0.489]
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+Epoch:   4%|         | 40/1000 [00:00<00:11, 81.53it/s, loss=0.483]
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+Epoch:   5%|         | 49/1000 [00:00<00:11, 81.95it/s, loss=0.459]
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+Epoch:   6%|         | 58/1000 [00:00<00:11, 83.96it/s, loss=0.457]
+Epoch:   6%|         | 58/1000 [00:00<00:11, 83.96it/s, loss=0.456]
+Epoch:   6%|         | 58/1000 [00:00<00:11, 83.96it/s, loss=0.455]
+Epoch:   6%|         | 58/1000 [00:00<00:11, 83.96it/s, loss=0.453]
+Epoch:   6%|         | 58/1000 [00:00<00:11, 83.96it/s, loss=0.453]
+Epoch:   6%|         | 58/1000 [00:00<00:11, 83.96it/s, loss=0.452]
+Epoch:   6%|         | 58/1000 [00:00<00:11, 83.96it/s, loss=0.45] 
+Epoch:   6%|         | 58/1000 [00:00<00:11, 83.96it/s, loss=0.449]
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+Epoch:   7%|         | 67/1000 [00:00<00:11, 82.62it/s, loss=0.441]
+Epoch:   7%|         | 67/1000 [00:00<00:11, 82.62it/s, loss=0.439]
+Epoch:   7%|         | 67/1000 [00:00<00:11, 82.62it/s, loss=0.437]
+Epoch:   7%|         | 67/1000 [00:00<00:11, 82.62it/s, loss=0.436]
+Epoch:   7%|         | 67/1000 [00:00<00:11, 82.62it/s, loss=0.434]
+Epoch:   7%|         | 67/1000 [00:00<00:11, 82.62it/s, loss=0.433]
+Epoch:   7%|         | 67/1000 [00:00<00:11, 82.62it/s, loss=0.432]
+Epoch:   8%|         | 76/1000 [00:00<00:11, 81.92it/s, loss=0.432]
+Epoch:   8%|         | 76/1000 [00:00<00:11, 81.92it/s, loss=0.432]
+Epoch:   8%|         | 76/1000 [00:00<00:11, 81.92it/s, loss=0.431]
+Epoch:   8%|         | 76/1000 [00:00<00:11, 81.92it/s, loss=0.431]
+Epoch:   8%|         | 76/1000 [00:01<00:11, 81.92it/s, loss=0.431]
+Epoch:   8%|         | 76/1000 [00:01<00:11, 81.92it/s, loss=0.431]
+Epoch:   8%|         | 76/1000 [00:01<00:11, 81.92it/s, loss=0.43] 
+Epoch:   8%|         | 76/1000 [00:01<00:11, 81.92it/s, loss=0.429]
+Epoch:   8%|         | 76/1000 [00:01<00:11, 81.92it/s, loss=0.428]
+Epoch:   8%|         | 76/1000 [00:01<00:11, 81.92it/s, loss=0.427]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.427]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.426]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.426]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.426]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.425]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.424]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.422]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.421]
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.42] 
+Epoch:   8%|         | 85/1000 [00:01<00:11, 82.27it/s, loss=0.42]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.42]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.418]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.418]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.416]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.416]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.415]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.415]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.386]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.401]
+Epoch:   9%|         | 94/1000 [00:01<00:11, 80.97it/s, loss=0.374]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.374]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.363]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.363]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.36] 
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.356]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.356]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.356]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.355]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.355]
+Epoch:  10%|         | 103/1000 [00:01<00:11, 79.05it/s, loss=0.355]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.355]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.349]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.351]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.351]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.35] 
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.348]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.349]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.349]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.347]
+Epoch:  11%|         | 112/1000 [00:01<00:10, 81.37it/s, loss=0.347]
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.347]
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.35] 
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.348]
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.35] 
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.348]
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.347]
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.347]
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.347]
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.346]
+Epoch:  12%|        | 121/1000 [00:01<00:10, 83.25it/s, loss=0.346]
+Epoch:  13%|        | 130/1000 [00:01<00:10, 83.58it/s, loss=0.346]
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+Epoch:  13%|        | 130/1000 [00:01<00:10, 83.58it/s, loss=0.346]
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+Epoch:  13%|        | 130/1000 [00:01<00:10, 83.58it/s, loss=0.347]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.347]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.345]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.346]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.345]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.346]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.347]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.348]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.347]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.346]
+Epoch:  14%|        | 139/1000 [00:01<00:10, 84.32it/s, loss=0.346]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.346]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.346]
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+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.344]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.343]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.344]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.345]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.345]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.344]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 85.21it/s, loss=0.344]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 84.52it/s, loss=0.344]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 84.52it/s, loss=0.346]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 84.52it/s, loss=0.345]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 84.52it/s, loss=0.345]
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+Epoch:  17%|        | 166/1000 [00:02<00:10, 80.57it/s, loss=0.342]
+Epoch:  17%|        | 166/1000 [00:02<00:10, 80.57it/s, loss=0.342]
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+Epoch:  17%|        | 166/1000 [00:02<00:10, 80.57it/s, loss=0.341]
+Epoch:  17%|        | 166/1000 [00:02<00:10, 80.57it/s, loss=0.341]
+Epoch:  17%|        | 166/1000 [00:02<00:10, 80.57it/s, loss=0.34] 
+Epoch:  17%|        | 166/1000 [00:02<00:10, 80.57it/s, loss=0.34]
+Epoch:  18%|        | 175/1000 [00:02<00:10, 80.80it/s, loss=0.34]
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+Epoch:  18%|        | 175/1000 [00:02<00:10, 80.80it/s, loss=0.339]
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+Epoch:  18%|        | 175/1000 [00:02<00:10, 80.80it/s, loss=0.339]
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+Epoch:  18%|        | 175/1000 [00:02<00:10, 80.80it/s, loss=0.339]
+Epoch:  18%|        | 175/1000 [00:02<00:10, 80.80it/s, loss=0.339]
+Epoch:  18%|        | 175/1000 [00:02<00:10, 80.80it/s, loss=0.339]
+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.339]
+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.339]
+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.339]
+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.339]
+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.34] 
+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.341]
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+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.34]
+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.339]
+Epoch:  18%|        | 185/1000 [00:02<00:09, 83.47it/s, loss=0.339]
+Epoch:  19%|        | 194/1000 [00:02<00:09, 82.28it/s, loss=0.339]
+Epoch:  19%|        | 194/1000 [00:02<00:09, 82.28it/s, loss=0.339]
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+Epoch:  19%|        | 194/1000 [00:02<00:09, 82.28it/s, loss=0.339]
+Epoch:  19%|        | 194/1000 [00:02<00:09, 82.28it/s, loss=0.339]
+Epoch:  19%|        | 194/1000 [00:02<00:09, 82.28it/s, loss=0.345]
+Epoch:  19%|        | 194/1000 [00:02<00:09, 82.28it/s, loss=0.298]
+Epoch:  19%|        | 194/1000 [00:02<00:09, 82.28it/s, loss=0.318]
+Epoch:  20%|        | 203/1000 [00:02<00:09, 83.74it/s, loss=0.318]
+Epoch:  20%|        | 203/1000 [00:02<00:09, 83.74it/s, loss=0.324]
+Epoch:  20%|        | 203/1000 [00:02<00:09, 83.74it/s, loss=0.329]
+Epoch:  20%|        | 203/1000 [00:02<00:09, 83.74it/s, loss=0.329]
+Epoch:  20%|        | 203/1000 [00:02<00:09, 83.74it/s, loss=0.334]
+Epoch:  20%|        | 203/1000 [00:02<00:09, 83.74it/s, loss=0.341]
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+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.349]
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.353]
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.354]
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.358]
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.357]
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.356]
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.359]
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.36] 
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.358]
+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.39it/s, loss=0.361]
+Epoch:  22%|       | 221/1000 [00:02<00:09, 80.13it/s, loss=0.361]
+Epoch:  22%|       | 221/1000 [00:02<00:09, 80.13it/s, loss=0.36] 
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+Epoch:  25%|       | 248/1000 [00:03<00:09, 77.98it/s, loss=0.342]
+Epoch:  25%|       | 248/1000 [00:03<00:09, 77.98it/s, loss=0.343]
+Epoch:  25%|       | 248/1000 [00:03<00:09, 77.98it/s, loss=0.343]
+Epoch:  25%|       | 248/1000 [00:03<00:09, 77.98it/s, loss=0.343]
+Epoch:  26%|       | 257/1000 [00:03<00:09, 81.18it/s, loss=0.343]
+Epoch:  26%|       | 257/1000 [00:03<00:09, 81.18it/s, loss=0.343]
+Epoch:  26%|       | 257/1000 [00:03<00:09, 81.18it/s, loss=0.344]
+Epoch:  26%|       | 257/1000 [00:03<00:09, 81.18it/s, loss=0.343]
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+Epoch:  26%|       | 257/1000 [00:03<00:09, 81.18it/s, loss=0.342]
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+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.343]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.342]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.342]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.343]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.342]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.342]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.342]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.341]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.341]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.41it/s, loss=0.341]
+Epoch:  28%|       | 276/1000 [00:03<00:08, 85.28it/s, loss=0.341]
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+Epoch:  28%|       | 276/1000 [00:03<00:08, 85.28it/s, loss=0.338]
+Epoch:  28%|       | 276/1000 [00:03<00:08, 85.28it/s, loss=0.338]
+Epoch:  28%|       | 276/1000 [00:03<00:08, 85.28it/s, loss=0.338]
+Epoch:  28%|       | 276/1000 [00:03<00:08, 85.28it/s, loss=0.338]
+Epoch:  28%|       | 276/1000 [00:03<00:08, 85.28it/s, loss=0.338]
+Epoch:  28%|       | 276/1000 [00:03<00:08, 85.28it/s, loss=0.338]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.338]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.338]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.337]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.337]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.337]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.337]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.337]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.337]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.336]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 83.52it/s, loss=0.336]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.336]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.336]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.336]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.336]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.335]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.335]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.335]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.366]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.364]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 83.06it/s, loss=0.32] 
+Epoch:  30%|       | 303/1000 [00:03<00:08, 82.01it/s, loss=0.32]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 82.01it/s, loss=0.305]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 82.01it/s, loss=0.3]  
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+Epoch:  30%|       | 303/1000 [00:03<00:08, 82.01it/s, loss=0.31] 
+Epoch:  30%|       | 303/1000 [00:03<00:08, 82.01it/s, loss=0.315]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 82.01it/s, loss=0.31] 
+Epoch:  30%|       | 303/1000 [00:03<00:08, 82.01it/s, loss=0.307]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 82.01it/s, loss=0.305]
+Epoch:  31%|       | 312/1000 [00:03<00:09, 75.41it/s, loss=0.305]
+Epoch:  31%|       | 312/1000 [00:03<00:09, 75.41it/s, loss=0.31] 
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+Epoch:  39%|      | 390/1000 [00:04<00:08, 73.41it/s, loss=0.333]
+Epoch:  39%|      | 390/1000 [00:04<00:08, 73.41it/s, loss=0.333]
+Epoch:  39%|      | 390/1000 [00:04<00:08, 73.41it/s, loss=0.333]
+Epoch:  39%|      | 390/1000 [00:05<00:08, 73.41it/s, loss=0.333]
+Epoch:  40%|      | 399/1000 [00:05<00:07, 76.71it/s, loss=0.333]
+Epoch:  40%|      | 399/1000 [00:05<00:07, 76.71it/s, loss=0.333]
+Epoch:  40%|      | 399/1000 [00:05<00:07, 76.71it/s, loss=0.373]
+Epoch:  40%|      | 399/1000 [00:05<00:07, 76.71it/s, loss=0.36] 
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+Epoch:  42%|     | 417/1000 [00:05<00:07, 80.09it/s, loss=0.321]
+Epoch:  42%|     | 417/1000 [00:05<00:07, 80.09it/s, loss=0.317]
+Epoch:  42%|     | 417/1000 [00:05<00:07, 80.09it/s, loss=0.318]
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+Epoch:  66%|   | 655/1000 [00:08<00:04, 81.07it/s, loss=0.318]
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+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.308]
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.308]
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.31] 
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.311]
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.31] 
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.31]
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.31]
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.31]
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.31]
+Epoch:  98%|| 981/1000 [00:12<00:00, 73.72it/s, loss=0.31]
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.31]
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.31]
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.31]
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.31]
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.31]
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.309]
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.31] 
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.31]
+Epoch:  99%|| 990/1000 [00:12<00:00, 75.49it/s, loss=0.309]
+Epoch: 100%|| 998/1000 [00:12<00:00, 76.55it/s, loss=0.309]
+Epoch: 100%|| 998/1000 [00:12<00:00, 76.55it/s, loss=0.308]
+Epoch: 100%|| 998/1000 [00:12<00:00, 76.55it/s, loss=0.309]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 78.48it/s, loss=0.309]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_fit[test_plugin1] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.942]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.905]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.847]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.794]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.748]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.718]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.701]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.701]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.687]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.673]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.644]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.625]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.616]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.609]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.602]
+Epoch:   1%|          | 7/1000 [00:00<00:16, 59.44it/s, loss=0.602]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.602]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.595]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.592]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.581]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.573]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.564]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.557]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.551]
+Epoch:   2%|         | 15/1000 [00:00<00:14, 68.48it/s, loss=0.546]
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.546]
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.541]
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.536]
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.53] 
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.524]
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.521]
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.52] 
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.515]
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.512]
+Epoch:   2%|         | 23/1000 [00:00<00:13, 70.98it/s, loss=0.512]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.512]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.509]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.504]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.501]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.498]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.494]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.492]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.491]
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.49] 
+Epoch:   3%|         | 32/1000 [00:00<00:12, 76.08it/s, loss=0.489]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.489]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.489]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.488]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.483]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.48] 
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.478]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.475]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.473]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.471]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 79.14it/s, loss=0.469]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.469]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.468]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.467]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.466]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.463]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.463]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.46] 
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.459]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.457]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 81.91it/s, loss=0.456]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.456]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.455]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.453]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.453]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.452]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.45] 
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.449]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.448]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.447]
+Epoch:   6%|         | 59/1000 [00:00<00:11, 81.05it/s, loss=0.445]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.445]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.443]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.441]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.439]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.437]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.436]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.434]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.433]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.432]
+Epoch:   7%|         | 68/1000 [00:00<00:11, 83.27it/s, loss=0.432]
+Epoch:   8%|         | 77/1000 [00:00<00:11, 80.60it/s, loss=0.432]
+Epoch:   8%|         | 77/1000 [00:00<00:11, 80.60it/s, loss=0.431]
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.431]
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.431]
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.431]
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.43] 
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.429]
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.428]
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.427]
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.426]
+Epoch:   8%|         | 77/1000 [00:01<00:11, 80.60it/s, loss=0.426]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.426]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.426]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.425]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.424]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.422]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.421]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.42] 
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.42]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.418]
+Epoch:   9%|         | 87/1000 [00:01<00:10, 83.77it/s, loss=0.418]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.418]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.416]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.416]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.415]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.415]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.386]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.401]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.374]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.363]
+Epoch:  10%|         | 96/1000 [00:01<00:11, 82.09it/s, loss=0.363]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.363]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.36] 
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.356]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.356]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.356]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.355]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.355]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.355]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.349]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.351]
+Epoch:  10%|         | 105/1000 [00:01<00:11, 78.79it/s, loss=0.351]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.351]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.35] 
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.348]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.349]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.349]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.347]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.347]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.35] 
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.348]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 82.78it/s, loss=0.35] 
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.35]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.348]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.347]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.347]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.347]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.346]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.346]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.345]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.346]
+Epoch:  12%|        | 124/1000 [00:01<00:10, 83.98it/s, loss=0.346]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.346]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.347]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.349]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.348]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.346]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.348]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.347]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.345]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.346]
+Epoch:  13%|        | 133/1000 [00:01<00:10, 83.35it/s, loss=0.345]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.345]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.346]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.347]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.348]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.347]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.346]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.346]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.346]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.345]
+Epoch:  14%|        | 142/1000 [00:01<00:10, 84.38it/s, loss=0.344]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.344]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.343]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.344]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.345]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.345]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.344]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.344]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.346]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.345]
+Epoch:  15%|        | 151/1000 [00:01<00:10, 83.46it/s, loss=0.345]
+Epoch:  16%|        | 160/1000 [00:01<00:09, 84.57it/s, loss=0.345]
+Epoch:  16%|        | 160/1000 [00:01<00:09, 84.57it/s, loss=0.344]
+Epoch:  16%|        | 160/1000 [00:01<00:09, 84.57it/s, loss=0.344]
+Epoch:  16%|        | 160/1000 [00:02<00:09, 84.57it/s, loss=0.344]
+Epoch:  16%|        | 160/1000 [00:02<00:09, 84.57it/s, loss=0.343]
+Epoch:  16%|        | 160/1000 [00:02<00:09, 84.57it/s, loss=0.343]
+Epoch:  16%|        | 160/1000 [00:02<00:09, 84.57it/s, loss=0.342]
+Epoch:  16%|        | 160/1000 [00:02<00:09, 84.57it/s, loss=0.343]
+Epoch:  16%|        | 160/1000 [00:02<00:09, 84.57it/s, loss=0.342]
+Epoch:  16%|        | 160/1000 [00:02<00:09, 84.57it/s, loss=0.342]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.342]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.342]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.341]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.341]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.341]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.34] 
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.34]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.34]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.339]
+Epoch:  17%|        | 169/1000 [00:02<00:09, 85.63it/s, loss=0.34] 
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.34]
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.339]
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.34] 
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.339]
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.339]
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.339]
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.339]
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.339]
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.339]
+Epoch:  18%|        | 178/1000 [00:02<00:09, 85.91it/s, loss=0.339]
+Epoch:  19%|        | 187/1000 [00:02<00:09, 85.74it/s, loss=0.339]
+Epoch:  19%|        | 187/1000 [00:02<00:09, 85.74it/s, loss=0.339]
+Epoch:  19%|        | 187/1000 [00:02<00:09, 85.74it/s, loss=0.34] 
+Epoch:  19%|        | 187/1000 [00:02<00:09, 85.74it/s, loss=0.341]
+Epoch:  19%|        | 187/1000 [00:02<00:09, 85.74it/s, loss=0.34] 
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+Epoch:  19%|        | 187/1000 [00:02<00:09, 85.74it/s, loss=0.339]
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+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.338]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.338]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.338]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.339]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.339]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.345]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.298]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.318]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.324]
+Epoch:  20%|        | 196/1000 [00:02<00:09, 86.84it/s, loss=0.329]
+Epoch:  20%|        | 205/1000 [00:02<00:09, 86.97it/s, loss=0.329]
+Epoch:  20%|        | 205/1000 [00:02<00:09, 86.97it/s, loss=0.329]
+Epoch:  20%|        | 205/1000 [00:02<00:09, 86.97it/s, loss=0.334]
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+Epoch:  20%|        | 205/1000 [00:02<00:09, 86.97it/s, loss=0.348]
+Epoch:  20%|        | 205/1000 [00:02<00:09, 86.97it/s, loss=0.349]
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+Epoch:  20%|        | 205/1000 [00:02<00:09, 86.97it/s, loss=0.354]
+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.354]
+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.358]
+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.357]
+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.356]
+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.359]
+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.36] 
+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.358]
+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.361]
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+Epoch:  21%|       | 214/1000 [00:02<00:09, 85.72it/s, loss=0.359]
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+Epoch:  22%|       | 224/1000 [00:02<00:08, 88.37it/s, loss=0.361]
+Epoch:  22%|       | 224/1000 [00:02<00:08, 88.37it/s, loss=0.361]
+Epoch:  22%|       | 224/1000 [00:02<00:08, 88.37it/s, loss=0.359]
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+Epoch:  23%|       | 233/1000 [00:02<00:09, 85.19it/s, loss=0.352]
+Epoch:  23%|       | 233/1000 [00:02<00:09, 85.19it/s, loss=0.351]
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+Epoch:  23%|       | 233/1000 [00:02<00:09, 85.19it/s, loss=0.347]
+Epoch:  23%|       | 233/1000 [00:02<00:09, 85.19it/s, loss=0.347]
+Epoch:  23%|       | 233/1000 [00:02<00:09, 85.19it/s, loss=0.347]
+Epoch:  23%|       | 233/1000 [00:02<00:09, 85.19it/s, loss=0.346]
+Epoch:  23%|       | 233/1000 [00:02<00:09, 85.19it/s, loss=0.346]
+Epoch:  24%|       | 242/1000 [00:02<00:09, 82.07it/s, loss=0.346]
+Epoch:  24%|       | 242/1000 [00:02<00:09, 82.07it/s, loss=0.346]
+Epoch:  24%|       | 242/1000 [00:02<00:09, 82.07it/s, loss=0.346]
+Epoch:  24%|       | 242/1000 [00:02<00:09, 82.07it/s, loss=0.346]
+Epoch:  24%|       | 242/1000 [00:02<00:09, 82.07it/s, loss=0.346]
+Epoch:  24%|       | 242/1000 [00:02<00:09, 82.07it/s, loss=0.345]
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+Epoch:  25%|       | 251/1000 [00:03<00:09, 83.05it/s, loss=0.342]
+Epoch:  25%|       | 251/1000 [00:03<00:09, 83.05it/s, loss=0.343]
+Epoch:  25%|       | 251/1000 [00:03<00:09, 83.05it/s, loss=0.343]
+Epoch:  25%|       | 251/1000 [00:03<00:09, 83.05it/s, loss=0.343]
+Epoch:  25%|       | 251/1000 [00:03<00:09, 83.05it/s, loss=0.343]
+Epoch:  25%|       | 251/1000 [00:03<00:09, 83.05it/s, loss=0.344]
+Epoch:  25%|       | 251/1000 [00:03<00:09, 83.05it/s, loss=0.343]
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+Epoch:  26%|       | 260/1000 [00:03<00:09, 79.42it/s, loss=0.343]
+Epoch:  26%|       | 260/1000 [00:03<00:09, 79.42it/s, loss=0.342]
+Epoch:  26%|       | 260/1000 [00:03<00:09, 79.42it/s, loss=0.342]
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+Epoch:  27%|       | 269/1000 [00:03<00:09, 81.22it/s, loss=0.343]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 81.22it/s, loss=0.342]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 81.22it/s, loss=0.342]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 81.22it/s, loss=0.342]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 81.22it/s, loss=0.341]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 81.22it/s, loss=0.341]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 81.22it/s, loss=0.341]
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+Epoch:  28%|       | 278/1000 [00:03<00:08, 82.57it/s, loss=0.338]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 82.57it/s, loss=0.338]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 82.57it/s, loss=0.338]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 82.57it/s, loss=0.338]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 82.57it/s, loss=0.338]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 82.57it/s, loss=0.338]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 82.57it/s, loss=0.338]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 82.57it/s, loss=0.337]
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+Epoch:  29%|       | 287/1000 [00:03<00:09, 73.02it/s, loss=0.337]
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+Epoch:  29%|       | 287/1000 [00:03<00:09, 73.02it/s, loss=0.337]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 73.02it/s, loss=0.337]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 73.02it/s, loss=0.337]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 73.02it/s, loss=0.336]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 73.02it/s, loss=0.336]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 73.02it/s, loss=0.336]
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+Epoch:  30%|       | 296/1000 [00:03<00:09, 76.51it/s, loss=0.336]
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+Epoch:  30%|       | 296/1000 [00:03<00:09, 76.51it/s, loss=0.335]
+Epoch:  30%|       | 296/1000 [00:03<00:09, 76.51it/s, loss=0.366]
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+Epoch:  30%|       | 296/1000 [00:03<00:09, 76.51it/s, loss=0.305]
+Epoch:  30%|       | 304/1000 [00:03<00:09, 77.12it/s, loss=0.305]
+Epoch:  30%|       | 304/1000 [00:03<00:09, 77.12it/s, loss=0.3]  
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+Epoch:  30%|       | 304/1000 [00:03<00:09, 77.12it/s, loss=0.305]
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+Epoch:  36%|      | 359/1000 [00:04<00:07, 83.87it/s, loss=0.33]
+Epoch:  36%|      | 359/1000 [00:04<00:07, 83.87it/s, loss=0.329]
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+Epoch:  36%|      | 359/1000 [00:04<00:07, 83.87it/s, loss=0.329]
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+Epoch:  37%|      | 369/1000 [00:04<00:07, 86.33it/s, loss=0.329]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 86.33it/s, loss=0.329]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 86.33it/s, loss=0.329]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 86.33it/s, loss=0.329]
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+Epoch:  38%|      | 378/1000 [00:04<00:07, 86.28it/s, loss=0.329]
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+Epoch:  38%|      | 378/1000 [00:04<00:07, 86.28it/s, loss=0.331]
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+Epoch:  96%|| 958/1000 [00:11<00:00, 77.23it/s, loss=0.307]
+Epoch:  96%|| 958/1000 [00:11<00:00, 77.23it/s, loss=0.307]
+Epoch:  96%|| 958/1000 [00:11<00:00, 77.23it/s, loss=0.306]
+Epoch:  96%|| 958/1000 [00:11<00:00, 77.23it/s, loss=0.306]
+Epoch:  96%|| 958/1000 [00:11<00:00, 77.23it/s, loss=0.306]
+Epoch:  96%|| 958/1000 [00:11<00:00, 77.23it/s, loss=0.306]
+Epoch:  96%|| 958/1000 [00:11<00:00, 77.23it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:11<00:00, 77.82it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:11<00:00, 77.82it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:11<00:00, 77.82it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.82it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.82it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.82it/s, loss=0.305]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.82it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.82it/s, loss=0.307]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.82it/s, loss=0.306]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.306]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.306]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.306]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.307]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.307]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.308]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.308]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.308]
+Epoch:  98%|| 975/1000 [00:12<00:00, 77.24it/s, loss=0.31] 
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.311]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.31] 
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.32it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.309]
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.31] 
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.309]
+Epoch:  99%|| 991/1000 [00:12<00:00, 75.50it/s, loss=0.308]
+Epoch: 100%|| 999/1000 [00:12<00:00, 75.66it/s, loss=0.308]
+Epoch: 100%|| 999/1000 [00:12<00:00, 75.66it/s, loss=0.309]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 80.56it/s, loss=0.309]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_fit[test_plugin2] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.942]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.905]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.847]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.794]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.748]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.718]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.701]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.687]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.673]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.673]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.644]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.625]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.616]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.609]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.602]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.602]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.595]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.592]
+Epoch:   1%|          | 9/1000 [00:00<00:12, 82.29it/s, loss=0.581]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.581]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.573]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.564]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.557]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.551]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.546]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.541]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.536]
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.53] 
+Epoch:   2%|         | 18/1000 [00:00<00:12, 81.72it/s, loss=0.524]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.524]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.521]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.52] 
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.515]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.512]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.512]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.509]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.504]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.501]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 83.42it/s, loss=0.498]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.498]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.494]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.492]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.491]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.49] 
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.489]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.489]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.488]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.483]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.71it/s, loss=0.48] 
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.48]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.478]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.475]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.473]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.471]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.469]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.468]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.467]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.466]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 83.47it/s, loss=0.463]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.463]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.463]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.46] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.459]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.457]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.456]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.455]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.453]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.453]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 83.61it/s, loss=0.452]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.452]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.45] 
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.449]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.448]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.447]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.445]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.443]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.441]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.439]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.437]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 80.85it/s, loss=0.436]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.436]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.434]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.433]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.432]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.432]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.431]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.431]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.431]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.431]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.43] 
+Epoch:   7%|         | 73/1000 [00:00<00:11, 84.05it/s, loss=0.429]
+Epoch:   8%|         | 83/1000 [00:00<00:10, 87.17it/s, loss=0.429]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.428]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.427]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.426]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.426]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.426]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.425]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.424]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.422]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 87.17it/s, loss=0.421]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.421]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.42] 
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.42]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.418]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.418]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.416]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.416]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.415]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.415]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 86.81it/s, loss=0.386]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.386]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.401]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.374]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.363]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.363]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.36] 
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.356]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.356]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.356]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 87.49it/s, loss=0.355]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.355]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.355]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.355]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.349]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.351]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.351]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.35] 
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.348]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.349]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.59it/s, loss=0.349]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.349]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.347]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.347]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.35] 
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.348]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.35] 
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.348]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.347]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.347]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 80.36it/s, loss=0.347]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.347]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.346]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.346]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.345]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.346]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.346]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.347]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.349]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.348]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 80.30it/s, loss=0.346]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.346]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.348]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.347]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.345]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.346]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.345]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.346]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.347]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.348]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 80.39it/s, loss=0.347]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.347]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.346]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.346]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.346]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.345]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.344]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.343]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.344]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.345]
+Epoch:  15%|        | 146/1000 [00:01<00:10, 80.62it/s, loss=0.345]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.345]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.346]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.345]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.345]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:10, 82.23it/s, loss=0.343]
+Epoch:  16%|        | 164/1000 [00:01<00:10, 82.16it/s, loss=0.343]
+Epoch:  16%|        | 164/1000 [00:01<00:10, 82.16it/s, loss=0.343]
+Epoch:  16%|        | 164/1000 [00:01<00:10, 82.16it/s, loss=0.342]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 82.16it/s, loss=0.343]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 82.16it/s, loss=0.342]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 82.16it/s, loss=0.342]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 82.16it/s, loss=0.342]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 82.16it/s, loss=0.341]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 82.16it/s, loss=0.341]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 82.16it/s, loss=0.341]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.341]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.34] 
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.34]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.34]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.339]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.34] 
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.339]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.34] 
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.339]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 83.36it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.34] 
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.341]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 85.05it/s, loss=0.34] 
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+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.34]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.339]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.339]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.339]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.338]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.338]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.338]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.339]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.44it/s, loss=0.339]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.339]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.345]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.298]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.318]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.324]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.329]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.329]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.334]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.341]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.16it/s, loss=0.34] 
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.34]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.343]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.348]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.349]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.353]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.354]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.358]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.357]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.356]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.77it/s, loss=0.359]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.359]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.36] 
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.358]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.361]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.36] 
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.361]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.359]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.361]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.361]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 82.51it/s, loss=0.359]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.81it/s, loss=0.359]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.81it/s, loss=0.357]
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+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.81it/s, loss=0.351]
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+Epoch:  24%|       | 236/1000 [00:02<00:09, 84.20it/s, loss=0.347]
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+Epoch:  24%|       | 236/1000 [00:02<00:09, 84.20it/s, loss=0.347]
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+Epoch:  24%|       | 236/1000 [00:02<00:09, 84.20it/s, loss=0.346]
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+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.342]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.344]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.343]
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+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.342]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 80.78it/s, loss=0.342]
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+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.68it/s, loss=0.343]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.68it/s, loss=0.342]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.68it/s, loss=0.342]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.68it/s, loss=0.343]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.68it/s, loss=0.342]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.68it/s, loss=0.342]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.51it/s, loss=0.342]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.51it/s, loss=0.342]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.51it/s, loss=0.341]
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+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.51it/s, loss=0.341]
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+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.51it/s, loss=0.338]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.338]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.338]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.338]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.338]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.338]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.338]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.337]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.337]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.337]
+Epoch:  28%|       | 281/1000 [00:03<00:08, 82.05it/s, loss=0.337]
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+Epoch:  29%|       | 290/1000 [00:03<00:08, 81.99it/s, loss=0.337]
+Epoch:  29%|       | 290/1000 [00:03<00:08, 81.99it/s, loss=0.337]
+Epoch:  29%|       | 290/1000 [00:03<00:08, 81.99it/s, loss=0.336]
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+Epoch:  29%|       | 290/1000 [00:03<00:08, 81.99it/s, loss=0.336]
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+Epoch:  29%|       | 290/1000 [00:03<00:08, 81.99it/s, loss=0.335]
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+Epoch:  30%|       | 299/1000 [00:03<00:08, 81.45it/s, loss=0.335]
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+Epoch:  30%|       | 299/1000 [00:03<00:08, 81.45it/s, loss=0.305]
+Epoch:  30%|       | 299/1000 [00:03<00:08, 81.45it/s, loss=0.3]  
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+Epoch:  30%|       | 299/1000 [00:03<00:08, 81.45it/s, loss=0.315]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 84.21it/s, loss=0.315]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 84.21it/s, loss=0.31] 
+Epoch:  31%|       | 309/1000 [00:03<00:08, 84.21it/s, loss=0.307]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 84.21it/s, loss=0.305]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 84.21it/s, loss=0.31] 
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+Epoch:  32%|      | 318/1000 [00:03<00:08, 82.07it/s, loss=0.312]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 82.07it/s, loss=0.311]
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+Epoch:  33%|      | 327/1000 [00:03<00:08, 78.98it/s, loss=0.311]
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+Epoch:  34%|      | 336/1000 [00:04<00:08, 81.49it/s, loss=0.314]
+Epoch:  34%|      | 336/1000 [00:04<00:08, 81.49it/s, loss=0.314]
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+Epoch:  34%|      | 336/1000 [00:04<00:08, 81.49it/s, loss=0.317]
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+Epoch:  34%|      | 336/1000 [00:04<00:08, 81.49it/s, loss=0.32]
+Epoch:  34%|      | 336/1000 [00:04<00:08, 81.49it/s, loss=0.319]
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+Epoch:  34%|      | 345/1000 [00:04<00:07, 83.20it/s, loss=0.321]
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+Epoch:  34%|      | 345/1000 [00:04<00:07, 83.20it/s, loss=0.322]
+Epoch:  34%|      | 345/1000 [00:04<00:07, 83.20it/s, loss=0.324]
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+Epoch:  34%|      | 345/1000 [00:04<00:07, 83.20it/s, loss=0.327]
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+Epoch:  61%|    | 610/1000 [00:07<00:05, 77.43it/s, loss=0.318]
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+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.311]
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.311]
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.311]
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.31] 
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.31]
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.308]
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.309]
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.308]
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.308]
+Epoch:  94%|| 940/1000 [00:11<00:00, 75.26it/s, loss=0.307]
+Epoch:  95%|| 949/1000 [00:11<00:00, 74.45it/s, loss=0.307]
+Epoch:  95%|| 949/1000 [00:11<00:00, 74.45it/s, loss=0.307]
+Epoch:  95%|| 949/1000 [00:11<00:00, 74.45it/s, loss=0.307]
+Epoch:  95%|| 949/1000 [00:11<00:00, 74.45it/s, loss=0.308]
+Epoch:  95%|| 949/1000 [00:11<00:00, 74.45it/s, loss=0.308]
+Epoch:  95%|| 949/1000 [00:11<00:00, 74.45it/s, loss=0.308]
+Epoch:  95%|| 949/1000 [00:11<00:00, 74.45it/s, loss=0.308]
+Epoch:  95%|| 949/1000 [00:12<00:00, 74.45it/s, loss=0.308]
+Epoch:  95%|| 949/1000 [00:12<00:00, 74.45it/s, loss=0.307]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.307]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.307]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.307]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.307]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.307]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.307]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.306]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.306]
+Epoch:  96%|| 957/1000 [00:12<00:00, 74.13it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.305]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.306]
+Epoch:  96%|| 965/1000 [00:12<00:00, 73.68it/s, loss=0.307]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.307]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.306]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.306]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.306]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.307]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.307]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.308]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.308]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.308]
+Epoch:  97%|| 974/1000 [00:12<00:00, 76.71it/s, loss=0.31] 
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.311]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.31] 
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.31]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.14it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.309]
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.31] 
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.31]
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.309]
+Epoch:  99%|| 991/1000 [00:12<00:00, 77.36it/s, loss=0.308]
+Epoch: 100%|| 999/1000 [00:12<00:00, 77.61it/s, loss=0.308]
+Epoch: 100%|| 999/1000 [00:12<00:00, 77.61it/s, loss=0.309]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 79.52it/s, loss=0.309]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_fit[test_plugin3] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.924]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.917]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.876]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.829]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.805]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.784]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.784]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.769]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.75] 
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.734]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.722]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.71] 
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.703]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.693]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.683]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.63it/s, loss=0.674]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.674]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.67] 
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.66]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.653]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.643]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.641]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.635]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.628]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.625]
+Epoch:   2%|         | 17/1000 [00:00<00:12, 81.76it/s, loss=0.621]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.621]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.617]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.612]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.605]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.603]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.6]  
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.595]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.591]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.589]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.585]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 84.47it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.579]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.576]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.573]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.571]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.567]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.563]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.56] 
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.557]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 86.57it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.552]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.548]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.545]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.544]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.543]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.541]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.539]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.535]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.54it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.532]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.53] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.528]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.526]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.525]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.522]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.52] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.19it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.517]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.515]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.512]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.511]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.511]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.51] 
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.509]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 85.04it/s, loss=0.507]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.507]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.506]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.505]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.505]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.503]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.502]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.501]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.5]  
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.5]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 85.14it/s, loss=0.498]
+Epoch:   8%|         | 81/1000 [00:00<00:10, 85.32it/s, loss=0.498]
+Epoch:   8%|         | 81/1000 [00:00<00:10, 85.32it/s, loss=0.497]
+Epoch:   8%|         | 81/1000 [00:00<00:10, 85.32it/s, loss=0.496]
+Epoch:   8%|         | 81/1000 [00:00<00:10, 85.32it/s, loss=0.495]
+Epoch:   8%|         | 81/1000 [00:00<00:10, 85.32it/s, loss=0.493]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 85.32it/s, loss=0.492]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 85.32it/s, loss=0.49] 
+Epoch:   8%|         | 81/1000 [00:01<00:10, 85.32it/s, loss=0.49]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 85.32it/s, loss=0.49]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 85.32it/s, loss=0.489]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 85.32it/s, loss=0.488]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.488]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.487]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.487]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.486]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.485]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.484]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.483]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.482]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.481]
+Epoch:   9%|         | 91/1000 [00:01<00:10, 87.61it/s, loss=0.48] 
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.48]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.41]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.423]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.413]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.408]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.414]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.408]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.407]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.403]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 87.66it/s, loss=0.412]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.412]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.411]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.41] 
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.407]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.409]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.414]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.417]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.414]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.41] 
+Epoch:  11%|         | 109/1000 [00:01<00:10, 86.88it/s, loss=0.412]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.412]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.411]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.41] 
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.41]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.408]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.407]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.407]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.408]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.407]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 86.64it/s, loss=0.406]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.406]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.403]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.402]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.403]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.404]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.404]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.404]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.404]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.404]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 85.15it/s, loss=0.402]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 82.87it/s, loss=0.402]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 82.87it/s, loss=0.402]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 82.87it/s, loss=0.402]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 82.87it/s, loss=0.402]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 82.87it/s, loss=0.402]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 82.87it/s, loss=0.401]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 82.87it/s, loss=0.4]  
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+Epoch:  14%|        | 145/1000 [00:01<00:10, 84.84it/s, loss=0.402]
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+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.99it/s, loss=0.399]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.99it/s, loss=0.399]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.99it/s, loss=0.399]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.99it/s, loss=0.398]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.99it/s, loss=0.398]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.99it/s, loss=0.398]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.99it/s, loss=0.398]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.99it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:01<00:10, 81.17it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:01<00:10, 81.17it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:01<00:10, 81.17it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:01<00:10, 81.17it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:01<00:10, 81.17it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 81.17it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 81.17it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 81.17it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 81.17it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:02<00:10, 81.17it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 81.26it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.4]  
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.4]  
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 82.09it/s, loss=0.4]  
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.4]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.4]  
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.4]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.4]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.398]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 83.94it/s, loss=0.336]
+Epoch:  20%|        | 201/1000 [00:02<00:09, 85.26it/s, loss=0.336]
+Epoch:  20%|        | 201/1000 [00:02<00:09, 85.26it/s, loss=0.42] 
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+Epoch:  20%|        | 201/1000 [00:02<00:09, 85.26it/s, loss=0.423]
+Epoch:  20%|        | 201/1000 [00:02<00:09, 85.26it/s, loss=0.423]
+Epoch:  20%|        | 201/1000 [00:02<00:09, 85.26it/s, loss=0.418]
+Epoch:  20%|        | 201/1000 [00:02<00:09, 85.26it/s, loss=0.419]
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+Epoch:  20%|        | 201/1000 [00:02<00:09, 85.26it/s, loss=0.408]
+Epoch:  20%|        | 201/1000 [00:02<00:09, 85.26it/s, loss=0.409]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.409]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.408]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.413]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.413]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.413]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.414]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.417]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.416]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.415]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.412]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 86.62it/s, loss=0.41] 
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+Epoch:  22%|       | 221/1000 [00:02<00:08, 88.19it/s, loss=0.407]
+Epoch:  22%|       | 221/1000 [00:02<00:08, 88.19it/s, loss=0.404]
+Epoch:  22%|       | 221/1000 [00:02<00:08, 88.19it/s, loss=0.403]
+Epoch:  22%|       | 221/1000 [00:02<00:08, 88.19it/s, loss=0.402]
+Epoch:  22%|       | 221/1000 [00:02<00:08, 88.19it/s, loss=0.402]
+Epoch:  22%|       | 221/1000 [00:02<00:08, 88.19it/s, loss=0.401]
+Epoch:  22%|       | 221/1000 [00:02<00:08, 88.19it/s, loss=0.401]
+Epoch:  23%|       | 230/1000 [00:02<00:09, 82.17it/s, loss=0.401]
+Epoch:  23%|       | 230/1000 [00:02<00:09, 82.17it/s, loss=0.399]
+Epoch:  23%|       | 230/1000 [00:02<00:09, 82.17it/s, loss=0.4]  
+Epoch:  23%|       | 230/1000 [00:02<00:09, 82.17it/s, loss=0.399]
+Epoch:  23%|       | 230/1000 [00:02<00:09, 82.17it/s, loss=0.4]  
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+Epoch:  23%|       | 230/1000 [00:02<00:09, 82.17it/s, loss=0.398]
+Epoch:  23%|       | 230/1000 [00:02<00:09, 82.17it/s, loss=0.397]
+Epoch:  24%|       | 239/1000 [00:02<00:09, 81.38it/s, loss=0.397]
+Epoch:  24%|       | 239/1000 [00:02<00:09, 81.38it/s, loss=0.397]
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+Epoch:  24%|       | 239/1000 [00:02<00:09, 81.38it/s, loss=0.396]
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+Epoch:  24%|       | 239/1000 [00:02<00:09, 81.38it/s, loss=0.396]
+Epoch:  24%|       | 239/1000 [00:02<00:09, 81.38it/s, loss=0.397]
+Epoch:  24%|       | 239/1000 [00:02<00:09, 81.38it/s, loss=0.396]
+Epoch:  25%|       | 248/1000 [00:02<00:09, 77.24it/s, loss=0.396]
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+Epoch:  25%|       | 248/1000 [00:02<00:09, 77.24it/s, loss=0.396]
+Epoch:  25%|       | 248/1000 [00:02<00:09, 77.24it/s, loss=0.397]
+Epoch:  25%|       | 248/1000 [00:03<00:09, 77.24it/s, loss=0.397]
+Epoch:  25%|       | 248/1000 [00:03<00:09, 77.24it/s, loss=0.396]
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+Epoch:  25%|       | 248/1000 [00:03<00:09, 77.24it/s, loss=0.395]
+Epoch:  26%|       | 256/1000 [00:03<00:09, 77.84it/s, loss=0.395]
+Epoch:  26%|       | 256/1000 [00:03<00:09, 77.84it/s, loss=0.395]
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+Epoch:  26%|       | 256/1000 [00:03<00:09, 77.84it/s, loss=0.394]
+Epoch:  26%|       | 265/1000 [00:03<00:09, 79.68it/s, loss=0.394]
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+Epoch:  26%|       | 265/1000 [00:03<00:09, 79.68it/s, loss=0.394]
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+Epoch:  26%|       | 265/1000 [00:03<00:09, 79.68it/s, loss=0.394]
+Epoch:  26%|       | 265/1000 [00:03<00:09, 79.68it/s, loss=0.394]
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+Epoch:  27%|       | 274/1000 [00:03<00:08, 80.95it/s, loss=0.395]
+Epoch:  27%|       | 274/1000 [00:03<00:08, 80.95it/s, loss=0.394]
+Epoch:  27%|       | 274/1000 [00:03<00:08, 80.95it/s, loss=0.394]
+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.394]
+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.393]
+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.393]
+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.393]
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+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.393]
+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.393]
+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.393]
+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.393]
+Epoch:  28%|       | 283/1000 [00:03<00:08, 80.11it/s, loss=0.394]
+Epoch:  29%|       | 292/1000 [00:03<00:08, 80.49it/s, loss=0.394]
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+Epoch:  29%|       | 292/1000 [00:03<00:08, 80.49it/s, loss=0.394]
+Epoch:  29%|       | 292/1000 [00:03<00:08, 80.49it/s, loss=0.394]
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+Epoch:  29%|       | 292/1000 [00:03<00:08, 80.49it/s, loss=0.394]
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+Epoch:  30%|       | 301/1000 [00:03<00:08, 78.80it/s, loss=0.376]
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+Epoch:  30%|       | 301/1000 [00:03<00:08, 78.80it/s, loss=0.379]
+Epoch:  30%|       | 301/1000 [00:03<00:08, 78.80it/s, loss=0.386]
+Epoch:  31%|       | 311/1000 [00:03<00:08, 82.70it/s, loss=0.386]
+Epoch:  31%|       | 311/1000 [00:03<00:08, 82.70it/s, loss=0.38] 
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+Epoch:  31%|       | 311/1000 [00:03<00:08, 82.70it/s, loss=0.379]
+Epoch:  31%|       | 311/1000 [00:03<00:08, 82.70it/s, loss=0.383]
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+Epoch:  31%|       | 311/1000 [00:03<00:08, 82.70it/s, loss=0.379]
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+Epoch:  31%|       | 311/1000 [00:03<00:08, 82.70it/s, loss=0.381]
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+Epoch:  32%|      | 320/1000 [00:03<00:08, 82.20it/s, loss=0.387]
+Epoch:  32%|      | 320/1000 [00:03<00:08, 82.20it/s, loss=0.386]
+Epoch:  32%|      | 320/1000 [00:03<00:08, 82.20it/s, loss=0.386]
+Epoch:  32%|      | 320/1000 [00:03<00:08, 82.20it/s, loss=0.387]
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+Epoch:  32%|      | 320/1000 [00:03<00:08, 82.20it/s, loss=0.385]
+Epoch:  32%|      | 320/1000 [00:03<00:08, 82.20it/s, loss=0.384]
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+Epoch:  32%|      | 320/1000 [00:03<00:08, 82.20it/s, loss=0.381]
+Epoch:  33%|      | 329/1000 [00:03<00:08, 82.89it/s, loss=0.381]
+Epoch:  33%|      | 329/1000 [00:03<00:08, 82.89it/s, loss=0.382]
+Epoch:  33%|      | 329/1000 [00:03<00:08, 82.89it/s, loss=0.384]
+Epoch:  33%|      | 329/1000 [00:03<00:08, 82.89it/s, loss=0.384]
+Epoch:  33%|      | 329/1000 [00:04<00:08, 82.89it/s, loss=0.382]
+Epoch:  33%|      | 329/1000 [00:04<00:08, 82.89it/s, loss=0.38] 
+Epoch:  33%|      | 329/1000 [00:04<00:08, 82.89it/s, loss=0.379]
+Epoch:  33%|      | 329/1000 [00:04<00:08, 82.89it/s, loss=0.38] 
+Epoch:  33%|      | 329/1000 [00:04<00:08, 82.89it/s, loss=0.378]
+Epoch:  33%|      | 329/1000 [00:04<00:08, 82.89it/s, loss=0.378]
+Epoch:  34%|      | 338/1000 [00:04<00:08, 76.74it/s, loss=0.378]
+Epoch:  34%|      | 338/1000 [00:04<00:08, 76.74it/s, loss=0.379]
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+Epoch:  35%|      | 347/1000 [00:04<00:08, 76.03it/s, loss=0.384]
+Epoch:  35%|      | 347/1000 [00:04<00:08, 76.03it/s, loss=0.384]
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+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.38]
+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.379]
+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.38] 
+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.38]
+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.38]
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+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.379]
+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.38] 
+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.379]
+Epoch:  38%|      | 384/1000 [00:04<00:07, 81.20it/s, loss=0.38] 
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+Epoch:  39%|      | 393/1000 [00:04<00:07, 83.42it/s, loss=0.381]
+Epoch:  39%|      | 393/1000 [00:04<00:07, 83.42it/s, loss=0.381]
+Epoch:  39%|      | 393/1000 [00:04<00:07, 83.42it/s, loss=0.381]
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+Epoch:  39%|      | 393/1000 [00:04<00:07, 83.42it/s, loss=0.38]
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+Epoch:  39%|      | 393/1000 [00:04<00:07, 83.42it/s, loss=0.371]
+Epoch:  39%|      | 393/1000 [00:04<00:07, 83.42it/s, loss=0.357]
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+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.357]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.359]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.356]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.357]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.358]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.358]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.358]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.359]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.358]
+Epoch:  92%|| 921/1000 [00:11<00:00, 82.18it/s, loss=0.357]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.357]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.355]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.354]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.354]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.353]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.352]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.353]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.352]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.353]
+Epoch:  93%|| 930/1000 [00:11<00:00, 79.33it/s, loss=0.353]
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.353]
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.352]
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.35] 
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.349]
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.35] 
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.35]
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.349]
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.349]
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.349]
+Epoch:  94%|| 939/1000 [00:11<00:00, 81.74it/s, loss=0.35] 
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.35]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.35]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.35]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.349]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.348]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.348]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.349]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.348]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.348]
+Epoch:  95%|| 948/1000 [00:11<00:00, 82.59it/s, loss=0.348]
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.348]
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.349]
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.35] 
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.351]
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.351]
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.35] 
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.35]
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.35]
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.349]
+Epoch:  96%|| 957/1000 [00:11<00:00, 83.56it/s, loss=0.35] 
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.35]
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.35] 
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.35] 
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.35]
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.351]
+Epoch:  97%|| 966/1000 [00:11<00:00, 81.25it/s, loss=0.351]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.351]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.35] 
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.352]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 84.66it/s, loss=0.353]
+Epoch:  98%|| 985/1000 [00:11<00:00, 84.18it/s, loss=0.353]
+Epoch:  98%|| 985/1000 [00:11<00:00, 84.18it/s, loss=0.353]
+Epoch:  98%|| 985/1000 [00:11<00:00, 84.18it/s, loss=0.352]
+Epoch:  98%|| 985/1000 [00:11<00:00, 84.18it/s, loss=0.353]
+Epoch:  98%|| 985/1000 [00:11<00:00, 84.18it/s, loss=0.352]
+Epoch:  98%|| 985/1000 [00:11<00:00, 84.18it/s, loss=0.352]
+Epoch:  98%|| 985/1000 [00:11<00:00, 84.18it/s, loss=0.352]
+Epoch:  98%|| 985/1000 [00:12<00:00, 84.18it/s, loss=0.351]
+Epoch:  98%|| 985/1000 [00:12<00:00, 84.18it/s, loss=0.352]
+Epoch:  98%|| 985/1000 [00:12<00:00, 84.18it/s, loss=0.352]
+Epoch:  99%|| 994/1000 [00:12<00:00, 81.19it/s, loss=0.352]
+Epoch:  99%|| 994/1000 [00:12<00:00, 81.19it/s, loss=0.352]
+Epoch:  99%|| 994/1000 [00:12<00:00, 81.19it/s, loss=0.352]
+Epoch:  99%|| 994/1000 [00:12<00:00, 81.19it/s, loss=0.352]
+Epoch:  99%|| 994/1000 [00:12<00:00, 81.19it/s, loss=0.352]
+Epoch:  99%|| 994/1000 [00:12<00:00, 81.19it/s, loss=0.352]
+Epoch:  99%|| 994/1000 [00:12<00:00, 81.19it/s, loss=0.352]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 82.67it/s, loss=0.352]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_fit[test_plugin4] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.924]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.917]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.876]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.829]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.805]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.784]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.784]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.769]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.75] 
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.734]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.722]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.71] 
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.703]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.693]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.683]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.674]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.96it/s, loss=0.67] 
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.67]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.66]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.653]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.643]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.641]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.635]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.628]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.625]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.621]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 87.68it/s, loss=0.617]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.617]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.612]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.605]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.603]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.6]  
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.595]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.591]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.589]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.585]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 75.57it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.579]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.576]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.573]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.571]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.567]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.563]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.56] 
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.557]
+Epoch:   4%|         | 36/1000 [00:00<00:12, 79.78it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.552]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.548]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.545]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.544]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.543]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.541]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.539]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.535]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 82.22it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.532]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.53] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.528]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.526]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.525]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.522]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.52] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 81.01it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.517]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.515]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.512]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.511]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.511]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.51] 
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.509]
+Epoch:   6%|         | 63/1000 [00:00<00:11, 82.03it/s, loss=0.507]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.507]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.506]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.505]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.505]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.503]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.502]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.501]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.5]  
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.5]
+Epoch:   7%|         | 72/1000 [00:00<00:11, 82.93it/s, loss=0.498]
+Epoch:   8%|         | 81/1000 [00:00<00:10, 83.55it/s, loss=0.498]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.497]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.496]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.495]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.493]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.492]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.49] 
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.49]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.49]
+Epoch:   8%|         | 81/1000 [00:01<00:10, 83.55it/s, loss=0.489]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.489]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.488]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.487]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.487]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.486]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.485]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.484]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.483]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.482]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.481]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.481]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.48] 
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.41]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.423]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.413]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.408]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.414]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.408]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.407]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 85.24it/s, loss=0.403]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.403]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.412]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.411]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.41] 
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.407]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.409]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.414]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.417]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.414]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 85.23it/s, loss=0.41] 
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.41]
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.412]
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.411]
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.41] 
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.41]
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.408]
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.407]
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.407]
+Epoch:  12%|        | 117/1000 [00:01<00:11, 76.31it/s, loss=0.408]
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+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.10it/s, loss=0.406]
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+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.10it/s, loss=0.402]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.10it/s, loss=0.403]
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+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.10it/s, loss=0.404]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.10it/s, loss=0.404]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.10it/s, loss=0.404]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.10it/s, loss=0.404]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.19it/s, loss=0.404]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.19it/s, loss=0.402]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.19it/s, loss=0.402]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.19it/s, loss=0.402]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.19it/s, loss=0.402]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.19it/s, loss=0.402]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.19it/s, loss=0.401]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.19it/s, loss=0.4]  
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+Epoch:  14%|        | 145/1000 [00:01<00:10, 82.30it/s, loss=0.402]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.402]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.401]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.399]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.399]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.399]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.398]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.398]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.398]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.398]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 85.24it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:01<00:09, 84.11it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.397]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.398]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 84.11it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 80.61it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.4]  
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.4]  
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.399]
+Epoch:  18%|        | 182/1000 [00:02<00:10, 81.33it/s, loss=0.4]  
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.4]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.4]  
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.4]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.4]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.399]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 82.15it/s, loss=0.398]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 80.88it/s, loss=0.398]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 80.88it/s, loss=0.336]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 80.88it/s, loss=0.42] 
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+Epoch:  20%|        | 200/1000 [00:02<00:09, 80.88it/s, loss=0.418]
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+Epoch:  20%|        | 200/1000 [00:02<00:09, 80.88it/s, loss=0.418]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.418]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.408]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.409]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.408]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.413]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.413]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.413]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.414]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.417]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 80.97it/s, loss=0.416]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.416]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.415]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.412]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.41] 
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.407]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.409]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.407]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.404]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.403]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 79.91it/s, loss=0.402]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.402]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.402]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.401]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.401]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.399]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.4]  
+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.399]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.4]  
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+Epoch:  23%|       | 227/1000 [00:02<00:09, 81.33it/s, loss=0.399]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 82.46it/s, loss=0.399]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 82.46it/s, loss=0.399]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 82.46it/s, loss=0.398]
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+Epoch:  24%|       | 245/1000 [00:02<00:09, 82.87it/s, loss=0.396]
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+Epoch:  24%|       | 245/1000 [00:03<00:09, 82.87it/s, loss=0.396]
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+Epoch:  24%|       | 245/1000 [00:03<00:09, 82.87it/s, loss=0.396]
+Epoch:  24%|       | 245/1000 [00:03<00:09, 82.87it/s, loss=0.397]
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+Epoch:  25%|       | 254/1000 [00:03<00:08, 83.85it/s, loss=0.395]
+Epoch:  25%|       | 254/1000 [00:03<00:08, 83.85it/s, loss=0.395]
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+Epoch:  25%|       | 254/1000 [00:03<00:08, 83.85it/s, loss=0.394]
+Epoch:  25%|       | 254/1000 [00:03<00:08, 83.85it/s, loss=0.394]
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+Epoch:  26%|       | 263/1000 [00:03<00:08, 81.93it/s, loss=0.394]
+Epoch:  26%|       | 263/1000 [00:03<00:08, 81.93it/s, loss=0.394]
+Epoch:  26%|       | 263/1000 [00:03<00:08, 81.93it/s, loss=0.394]
+Epoch:  26%|       | 263/1000 [00:03<00:08, 81.93it/s, loss=0.394]
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+Epoch:  26%|       | 263/1000 [00:03<00:08, 81.93it/s, loss=0.393]
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+Epoch:  27%|       | 272/1000 [00:03<00:09, 80.11it/s, loss=0.394]
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+Epoch:  28%|       | 282/1000 [00:03<00:08, 83.80it/s, loss=0.394]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 83.80it/s, loss=0.393]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 83.80it/s, loss=0.393]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 83.80it/s, loss=0.393]
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+Epoch:  28%|       | 282/1000 [00:03<00:08, 83.80it/s, loss=0.393]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 83.80it/s, loss=0.393]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 83.80it/s, loss=0.393]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 83.80it/s, loss=0.393]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.393]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 84.90it/s, loss=0.394]
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+Epoch:  30%|       | 300/1000 [00:03<00:08, 80.17it/s, loss=0.364]
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+Epoch:  30%|       | 300/1000 [00:03<00:08, 80.17it/s, loss=0.378]
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+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.383]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.379]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.386]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.38] 
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+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.379]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.383]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.381]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.379]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 82.67it/s, loss=0.379]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 83.42it/s, loss=0.379]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 83.42it/s, loss=0.381]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 83.42it/s, loss=0.385]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 83.42it/s, loss=0.387]
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+Epoch:  32%|      | 318/1000 [00:03<00:08, 83.42it/s, loss=0.385]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 83.42it/s, loss=0.384]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 83.42it/s, loss=0.382]
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+Epoch:  33%|      | 328/1000 [00:03<00:07, 87.37it/s, loss=0.381]
+Epoch:  33%|      | 328/1000 [00:04<00:07, 87.37it/s, loss=0.382]
+Epoch:  33%|      | 328/1000 [00:04<00:07, 87.37it/s, loss=0.384]
+Epoch:  33%|      | 328/1000 [00:04<00:07, 87.37it/s, loss=0.384]
+Epoch:  33%|      | 328/1000 [00:04<00:07, 87.37it/s, loss=0.382]
+Epoch:  33%|      | 328/1000 [00:04<00:07, 87.37it/s, loss=0.38] 
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+Epoch:  33%|      | 328/1000 [00:04<00:07, 87.37it/s, loss=0.38] 
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+Epoch:  34%|      | 337/1000 [00:04<00:07, 87.25it/s, loss=0.378]
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+Epoch:  37%|      | 374/1000 [00:04<00:07, 80.92it/s, loss=0.38] 
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+Epoch:  38%|      | 383/1000 [00:04<00:07, 80.09it/s, loss=0.38]
+Epoch:  38%|      | 383/1000 [00:04<00:07, 80.09it/s, loss=0.379]
+Epoch:  38%|      | 383/1000 [00:04<00:07, 80.09it/s, loss=0.38] 
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+Epoch:  90%| | 902/1000 [00:10<00:01, 85.36it/s, loss=0.374]
+Epoch:  90%| | 902/1000 [00:10<00:01, 85.36it/s, loss=0.367]
+Epoch:  90%| | 902/1000 [00:10<00:01, 85.36it/s, loss=0.366]
+Epoch:  90%| | 902/1000 [00:11<00:01, 85.36it/s, loss=0.366]
+Epoch:  90%| | 902/1000 [00:11<00:01, 85.36it/s, loss=0.366]
+Epoch:  90%| | 902/1000 [00:11<00:01, 85.36it/s, loss=0.361]
+Epoch:  90%| | 902/1000 [00:11<00:01, 85.36it/s, loss=0.363]
+Epoch:  90%| | 902/1000 [00:11<00:01, 85.36it/s, loss=0.362]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.362]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.362]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.363]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.363]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.363]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.361]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.364]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.362]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.361]
+Epoch:  91%| | 911/1000 [00:11<00:01, 84.55it/s, loss=0.357]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.357]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.357]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.359]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.356]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.357]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.358]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.358]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.358]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.359]
+Epoch:  92%|| 920/1000 [00:11<00:00, 82.07it/s, loss=0.358]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.358]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.357]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.355]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.354]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.354]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.353]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.352]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.353]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.352]
+Epoch:  93%|| 929/1000 [00:11<00:00, 80.84it/s, loss=0.353]
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.353]
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.353]
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.352]
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.35] 
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.349]
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.35] 
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.35]
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.349]
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.349]
+Epoch:  94%|| 938/1000 [00:11<00:00, 78.43it/s, loss=0.349]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.349]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.35] 
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.35]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.35]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.349]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.348]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.348]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.349]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.348]
+Epoch:  95%|| 947/1000 [00:11<00:00, 78.78it/s, loss=0.348]
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.348]
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.348]
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.349]
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.35] 
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.351]
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.351]
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.35] 
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.35]
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.35]
+Epoch:  96%|| 956/1000 [00:11<00:00, 79.71it/s, loss=0.349]
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.349]
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.35] 
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.349]
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.35] 
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.349]
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.349]
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.349]
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.349]
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.35] 
+Epoch:  96%|| 965/1000 [00:11<00:00, 80.66it/s, loss=0.35]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.35]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.351]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.351]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.35] 
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.352]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 78.99it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 79.50it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 79.50it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 79.50it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.50it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.50it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.50it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.50it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.50it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:12<00:00, 79.50it/s, loss=0.352]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.352]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.351]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.352]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.352]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.352]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.352]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.352]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.352]
+Epoch:  99%|| 991/1000 [00:12<00:00, 79.18it/s, loss=0.352]
+Epoch: 100%|| 999/1000 [00:12<00:00, 78.31it/s, loss=0.352]
+Epoch: 100%|| 999/1000 [00:12<00:00, 78.31it/s, loss=0.352]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 82.01it/s, loss=0.352]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_fit[test_plugin5] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.924]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.917]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.876]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.829]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.805]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.784]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.769]
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.769]
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.75] 
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.734]
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.722]
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.71] 
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.703]
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.693]
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.683]
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.674]
+Epoch:   1%|          | 9/1000 [00:00<00:11, 82.64it/s, loss=0.67] 
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.67]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.66]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.653]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.643]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.641]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.635]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.628]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.625]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.621]
+Epoch:   2%|         | 18/1000 [00:00<00:13, 75.46it/s, loss=0.617]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.617]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.612]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.605]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.603]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.6]  
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.595]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.591]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.589]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.585]
+Epoch:   3%|         | 27/1000 [00:00<00:12, 78.81it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.579]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.576]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.573]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.571]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.567]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.563]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.56] 
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.557]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 82.13it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.552]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.548]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.545]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.544]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.543]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.541]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.539]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.535]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.533]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 79.36it/s, loss=0.533]
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.533]
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.532]
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.53] 
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.528]
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.526]
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.525]
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.522]
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.52] 
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.519]
+Epoch:   6%|         | 55/1000 [00:00<00:11, 83.88it/s, loss=0.519]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.519]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.517]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.515]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.512]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.511]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.511]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.51] 
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.509]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.507]
+Epoch:   6%|         | 64/1000 [00:00<00:10, 85.24it/s, loss=0.506]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.506]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.505]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.505]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.503]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.502]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.501]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.5]  
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.5]
+Epoch:   7%|         | 73/1000 [00:00<00:11, 82.53it/s, loss=0.498]
+Epoch:   7%|         | 73/1000 [00:01<00:11, 82.53it/s, loss=0.497]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.497]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.496]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.495]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.493]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.492]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.49] 
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.49]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.49]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.489]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.488]
+Epoch:   8%|         | 82/1000 [00:01<00:11, 82.53it/s, loss=0.487]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.487]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.487]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.486]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.485]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.484]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.483]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.482]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.481]
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.48] 
+Epoch:   9%|         | 92/1000 [00:01<00:10, 85.68it/s, loss=0.41]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.41]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.423]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.413]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.408]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.414]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.408]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.407]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.403]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.412]
+Epoch:  10%|         | 101/1000 [00:01<00:10, 86.02it/s, loss=0.411]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.411]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.41] 
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.407]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.409]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.414]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.417]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.414]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.41] 
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.412]
+Epoch:  11%|         | 110/1000 [00:01<00:10, 85.97it/s, loss=0.411]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.411]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.41] 
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.41]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.408]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.407]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.407]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.408]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.407]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.406]
+Epoch:  12%|        | 119/1000 [00:01<00:10, 86.46it/s, loss=0.403]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.403]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.402]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.403]
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+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.402]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.402]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 85.26it/s, loss=0.402]
+Epoch:  14%|        | 138/1000 [00:01<00:09, 86.98it/s, loss=0.402]
+Epoch:  14%|        | 138/1000 [00:01<00:09, 86.98it/s, loss=0.402]
+Epoch:  14%|        | 138/1000 [00:01<00:09, 86.98it/s, loss=0.402]
+Epoch:  14%|        | 138/1000 [00:01<00:09, 86.98it/s, loss=0.401]
+Epoch:  14%|        | 138/1000 [00:01<00:09, 86.98it/s, loss=0.4]  
+Epoch:  14%|        | 138/1000 [00:01<00:09, 86.98it/s, loss=0.4]
+Epoch:  14%|        | 138/1000 [00:01<00:09, 86.98it/s, loss=0.4]
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+Epoch:  14%|        | 138/1000 [00:01<00:09, 86.98it/s, loss=0.4]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.4]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.399]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.399]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.399]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.399]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.401]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.401]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.401]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.402]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.401]
+Epoch:  15%|        | 147/1000 [00:01<00:10, 84.87it/s, loss=0.399]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.399]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.399]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.399]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 87.90it/s, loss=0.398]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 87.13it/s, loss=0.398]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 87.13it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 87.13it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 87.13it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:02<00:09, 87.13it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:02<00:09, 87.13it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:02<00:09, 87.13it/s, loss=0.398]
+Epoch:  17%|        | 166/1000 [00:02<00:09, 87.13it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:02<00:09, 87.13it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:02<00:09, 87.13it/s, loss=0.397]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.397]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.398]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.398]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.398]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 82.90it/s, loss=0.4]  
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.4]
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.4]  
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.4]  
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:09, 82.00it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.4]  
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.4]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.4]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.398]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.336]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.42] 
+Epoch:  19%|        | 193/1000 [00:02<00:09, 81.47it/s, loss=0.434]
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+Epoch:  20%|        | 203/1000 [00:02<00:09, 85.02it/s, loss=0.408]
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+Epoch:  20%|        | 203/1000 [00:02<00:09, 85.02it/s, loss=0.408]
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+Epoch:  21%|       | 213/1000 [00:02<00:08, 88.32it/s, loss=0.413]
+Epoch:  21%|       | 213/1000 [00:02<00:08, 88.32it/s, loss=0.413]
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+Epoch:  21%|       | 213/1000 [00:02<00:08, 88.32it/s, loss=0.417]
+Epoch:  21%|       | 213/1000 [00:02<00:08, 88.32it/s, loss=0.416]
+Epoch:  21%|       | 213/1000 [00:02<00:08, 88.32it/s, loss=0.415]
+Epoch:  21%|       | 213/1000 [00:02<00:08, 88.32it/s, loss=0.412]
+Epoch:  21%|       | 213/1000 [00:02<00:08, 88.32it/s, loss=0.41] 
+Epoch:  21%|       | 213/1000 [00:02<00:08, 88.32it/s, loss=0.407]
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+Epoch:  22%|       | 222/1000 [00:02<00:09, 82.74it/s, loss=0.407]
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+Epoch:  22%|       | 222/1000 [00:02<00:09, 82.74it/s, loss=0.403]
+Epoch:  22%|       | 222/1000 [00:02<00:09, 82.74it/s, loss=0.402]
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+Epoch:  22%|       | 222/1000 [00:02<00:09, 82.74it/s, loss=0.401]
+Epoch:  22%|       | 222/1000 [00:02<00:09, 82.74it/s, loss=0.399]
+Epoch:  23%|       | 231/1000 [00:02<00:09, 84.57it/s, loss=0.399]
+Epoch:  23%|       | 231/1000 [00:02<00:09, 84.57it/s, loss=0.4]  
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+Epoch:  30%|       | 296/1000 [00:03<00:08, 86.09it/s, loss=0.394]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 86.09it/s, loss=0.394]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 86.09it/s, loss=0.371]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 86.09it/s, loss=0.389]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 86.09it/s, loss=0.38] 
+Epoch:  30%|       | 296/1000 [00:03<00:08, 86.09it/s, loss=0.376]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 86.09it/s, loss=0.364]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.364]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.37] 
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.379]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.378]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.383]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.379]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.386]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.38] 
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.381]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.92it/s, loss=0.379]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.379]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.383]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.381]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.379]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.379]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.381]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.385]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.387]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.386]
+Epoch:  31%|      | 314/1000 [00:03<00:08, 83.67it/s, loss=0.386]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.386]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.387]
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+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.385]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.384]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.382]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.381]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.382]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.384]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.384]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 84.97it/s, loss=0.382]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.61it/s, loss=0.382]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.61it/s, loss=0.38] 
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.61it/s, loss=0.379]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.61it/s, loss=0.38] 
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.61it/s, loss=0.378]
+Epoch:  33%|      | 333/1000 [00:04<00:07, 85.61it/s, loss=0.378]
+Epoch:  33%|      | 333/1000 [00:04<00:07, 85.61it/s, loss=0.379]
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+Epoch:  33%|      | 333/1000 [00:04<00:07, 85.61it/s, loss=0.38]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 82.48it/s, loss=0.38]
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+Epoch:  34%|      | 342/1000 [00:04<00:07, 82.48it/s, loss=0.383]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 82.48it/s, loss=0.384]
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+Epoch:  35%|      | 351/1000 [00:04<00:08, 80.77it/s, loss=0.384]
+Epoch:  35%|      | 351/1000 [00:04<00:08, 80.77it/s, loss=0.384]
+Epoch:  35%|      | 351/1000 [00:04<00:08, 80.77it/s, loss=0.384]
+Epoch:  35%|      | 351/1000 [00:04<00:08, 80.77it/s, loss=0.383]
+Epoch:  35%|      | 351/1000 [00:04<00:08, 80.77it/s, loss=0.382]
+Epoch:  35%|      | 351/1000 [00:04<00:08, 80.77it/s, loss=0.383]
+Epoch:  35%|      | 351/1000 [00:04<00:08, 80.77it/s, loss=0.382]
+Epoch:  35%|      | 351/1000 [00:04<00:08, 80.77it/s, loss=0.382]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.382]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.382]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.382]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.383]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.383]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.384]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.385]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.384]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.384]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 84.21it/s, loss=0.384]
+Epoch:  37%|      | 370/1000 [00:04<00:07, 81.86it/s, loss=0.384]
+Epoch:  37%|      | 370/1000 [00:04<00:07, 81.86it/s, loss=0.383]
+Epoch:  37%|      | 370/1000 [00:04<00:07, 81.86it/s, loss=0.383]
+Epoch:  37%|      | 370/1000 [00:04<00:07, 81.86it/s, loss=0.382]
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+Epoch:  37%|      | 370/1000 [00:04<00:07, 81.86it/s, loss=0.381]
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+Epoch:  56%|    | 563/1000 [00:06<00:05, 80.11it/s, loss=0.373]
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+Epoch:  57%|    | 572/1000 [00:06<00:05, 81.33it/s, loss=0.372]
+Epoch:  57%|    | 572/1000 [00:06<00:05, 81.33it/s, loss=0.371]
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+Epoch:  58%|    | 581/1000 [00:06<00:05, 82.13it/s, loss=0.371]
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+Epoch:  58%|    | 581/1000 [00:07<00:05, 82.13it/s, loss=0.37] 
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+Epoch:  59%|    | 591/1000 [00:07<00:04, 85.32it/s, loss=0.37]
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+Epoch:  88%| | 882/1000 [00:10<00:01, 77.85it/s, loss=0.362]
+Epoch:  88%| | 882/1000 [00:10<00:01, 77.85it/s, loss=0.362]
+Epoch:  88%| | 882/1000 [00:10<00:01, 77.85it/s, loss=0.362]
+Epoch:  88%| | 882/1000 [00:10<00:01, 77.85it/s, loss=0.362]
+Epoch:  88%| | 882/1000 [00:10<00:01, 77.85it/s, loss=0.362]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.362]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.362]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.363]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.362]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.361]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.361]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.361]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.361]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.361]
+Epoch:  89%| | 890/1000 [00:10<00:01, 76.82it/s, loss=0.362]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.362]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.362]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.418]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.37] 
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.385]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.374]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.367]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.366]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.366]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.366]
+Epoch:  90%| | 899/1000 [00:10<00:01, 79.99it/s, loss=0.361]
+Epoch:  91%| | 909/1000 [00:10<00:01, 84.27it/s, loss=0.361]
+Epoch:  91%| | 909/1000 [00:10<00:01, 84.27it/s, loss=0.363]
+Epoch:  91%| | 909/1000 [00:10<00:01, 84.27it/s, loss=0.362]
+Epoch:  91%| | 909/1000 [00:11<00:01, 84.27it/s, loss=0.362]
+Epoch:  91%| | 909/1000 [00:11<00:01, 84.27it/s, loss=0.363]
+Epoch:  91%| | 909/1000 [00:11<00:01, 84.27it/s, loss=0.363]
+Epoch:  91%| | 909/1000 [00:11<00:01, 84.27it/s, loss=0.363]
+Epoch:  91%| | 909/1000 [00:11<00:01, 84.27it/s, loss=0.361]
+Epoch:  91%| | 909/1000 [00:11<00:01, 84.27it/s, loss=0.364]
+Epoch:  91%| | 909/1000 [00:11<00:01, 84.27it/s, loss=0.362]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.362]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.361]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.357]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.357]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.359]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.356]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.357]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.358]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.358]
+Epoch:  92%|| 918/1000 [00:11<00:01, 78.40it/s, loss=0.358]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.358]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.359]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.358]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.357]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.355]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.354]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.354]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.353]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.352]
+Epoch:  93%|| 927/1000 [00:11<00:00, 79.01it/s, loss=0.353]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.353]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.352]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.353]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.353]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.352]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.35] 
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.349]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.35] 
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.35]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.15it/s, loss=0.349]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.349]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.349]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.349]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.35] 
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.35]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.35]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.349]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.348]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.348]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.50it/s, loss=0.349]
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.349]
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.348]
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.348]
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.348]
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.349]
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.35] 
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.351]
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.351]
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.35] 
+Epoch:  95%|| 954/1000 [00:11<00:00, 79.75it/s, loss=0.35]
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.35]
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.35]
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.349]
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.35] 
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.349]
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.35] 
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.349]
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.349]
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.349]
+Epoch:  96%|| 963/1000 [00:11<00:00, 78.61it/s, loss=0.349]
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.349]
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.35] 
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.35]
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.351]
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.351]
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.35] 
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.352]
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.353]
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.353]
+Epoch:  97%|| 972/1000 [00:11<00:00, 80.71it/s, loss=0.353]
+Epoch:  98%|| 981/1000 [00:11<00:00, 76.55it/s, loss=0.353]
+Epoch:  98%|| 981/1000 [00:11<00:00, 76.55it/s, loss=0.353]
+Epoch:  98%|| 981/1000 [00:11<00:00, 76.55it/s, loss=0.353]
+Epoch:  98%|| 981/1000 [00:11<00:00, 76.55it/s, loss=0.353]
+Epoch:  98%|| 981/1000 [00:11<00:00, 76.55it/s, loss=0.353]
+Epoch:  98%|| 981/1000 [00:11<00:00, 76.55it/s, loss=0.353]
+Epoch:  98%|| 981/1000 [00:11<00:00, 76.55it/s, loss=0.352]
+Epoch:  98%|| 981/1000 [00:11<00:00, 76.55it/s, loss=0.353]
+Epoch:  98%|| 981/1000 [00:12<00:00, 76.55it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.351]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch:  99%|| 989/1000 [00:12<00:00, 73.23it/s, loss=0.352]
+Epoch: 100%|| 998/1000 [00:12<00:00, 76.92it/s, loss=0.352]
+Epoch: 100%|| 998/1000 [00:12<00:00, 76.92it/s, loss=0.352]
+Epoch: 100%|| 998/1000 [00:12<00:00, 76.92it/s, loss=0.352]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 82.34it/s, loss=0.352]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_early_stop 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.02, val=0.987]
+Epoch:   0%|          | 1/1000 [00:00<02:51,  5.82it/s, loss=1.02, val=0.987]
+Epoch:   0%|          | 1/1000 [00:00<02:51,  5.82it/s, loss=0.969, val=0.997]
+Epoch:   0%|          | 2/1000 [00:00<02:44,  6.05it/s, loss=0.969, val=0.997]
+Epoch:   0%|          | 2/1000 [00:00<02:44,  6.05it/s, loss=0.957, val=0.948]
+Epoch:   0%|          | 3/1000 [00:00<02:43,  6.09it/s, loss=0.957, val=0.948]
+Epoch:   0%|          | 3/1000 [00:00<02:43,  6.09it/s, loss=0.914, val=0.997]
+Epoch:   0%|          | 4/1000 [00:00<02:42,  6.15it/s, loss=0.914, val=0.997]
+Epoch:   0%|          | 4/1000 [00:00<02:42,  6.15it/s, loss=0.859, val=0.943]
+Epoch:   0%|          | 5/1000 [00:00<02:42,  6.13it/s, loss=0.859, val=0.943]
+Epoch:   0%|          | 5/1000 [00:00<02:42,  6.13it/s, loss=0.797, val=0.922]
+Epoch:   1%|          | 6/1000 [00:00<02:43,  6.07it/s, loss=0.797, val=0.922]
+Epoch:   1%|          | 6/1000 [00:01<02:43,  6.07it/s, loss=0.772, val=0.952]
+Epoch:   1%|          | 7/1000 [00:01<02:41,  6.13it/s, loss=0.772, val=0.952]
+Epoch:   1%|          | 7/1000 [00:01<02:41,  6.13it/s, loss=0.757, val=0.987]
+Epoch:   1%|          | 8/1000 [00:01<02:39,  6.20it/s, loss=0.757, val=0.987]
+Epoch:   1%|          | 8/1000 [00:01<02:39,  6.20it/s, loss=0.746, val=0.987]
+Epoch:   1%|          | 9/1000 [00:01<02:42,  6.09it/s, loss=0.746, val=0.987]
+Epoch:   1%|          | 9/1000 [00:01<02:42,  6.09it/s, loss=0.719, val=0.995]
+Epoch:   1%|          | 10/1000 [00:01<02:41,  6.12it/s, loss=0.719, val=0.995]
+Epoch:   1%|          | 10/1000 [00:01<02:41,  6.12it/s, loss=0.7, val=0.988]  
+Epoch:   1%|          | 11/1000 [00:01<02:42,  6.08it/s, loss=0.7, val=0.988]
+Epoch:   1%|          | 11/1000 [00:01<02:42,  6.08it/s, loss=0.688, val=0.968]
+Epoch:   1%|          | 12/1000 [00:01<02:40,  6.16it/s, loss=0.688, val=0.968]
+Epoch:   1%|          | 12/1000 [00:02<02:40,  6.16it/s, loss=0.68, val=1]     
+Epoch:   1%|         | 13/1000 [00:02<02:39,  6.18it/s, loss=0.68, val=1]
+Epoch:   1%|         | 13/1000 [00:02<02:39,  6.18it/s, loss=0.678, val=1]
+Epoch:   1%|         | 14/1000 [00:02<02:41,  6.10it/s, loss=0.678, val=1]
+Epoch:   1%|         | 14/1000 [00:02<02:41,  6.10it/s, loss=0.672, val=0.997]
+Epoch:   2%|         | 15/1000 [00:02<02:42,  6.07it/s, loss=0.672, val=0.997]
+Epoch:   2%|         | 15/1000 [00:02<02:42,  6.07it/s, loss=0.661, val=1]    
+Epoch:   2%|         | 16/1000 [00:02<02:39,  6.15it/s, loss=0.661, val=1]
+Epoch:   2%|         | 16/1000 [00:02<02:39,  6.15it/s, loss=0.655, val=1]
+Epoch:   2%|         | 17/1000 [00:02<02:41,  6.09it/s, loss=0.655, val=1]
+Epoch:   2%|         | 17/1000 [00:02<02:41,  6.09it/s, loss=0.649, val=0.967]
+Epoch:   2%|         | 18/1000 [00:02<02:40,  6.10it/s, loss=0.649, val=0.967]
+Epoch:   2%|         | 18/1000 [00:03<02:40,  6.10it/s, loss=0.64, val=0.983] 
+Epoch:   2%|         | 19/1000 [00:03<02:40,  6.13it/s, loss=0.64, val=0.983]
+Epoch:   2%|         | 19/1000 [00:03<02:40,  6.13it/s, loss=0.634, val=1]   
+Epoch:   2%|         | 20/1000 [00:03<02:40,  6.10it/s, loss=0.634, val=1]
+Epoch:   2%|         | 20/1000 [00:03<02:40,  6.10it/s, loss=0.63, val=0.967]
+Epoch:   2%|         | 21/1000 [00:03<02:40,  6.12it/s, loss=0.63, val=0.967]
+Epoch:   2%|         | 21/1000 [00:03<02:40,  6.12it/s, loss=0.629, val=0.88]
+Epoch:   2%|         | 22/1000 [00:03<02:39,  6.14it/s, loss=0.629, val=0.88]
+Epoch:   2%|         | 22/1000 [00:03<02:39,  6.14it/s, loss=0.624, val=0.948]
+Epoch:   2%|         | 23/1000 [00:03<02:39,  6.14it/s, loss=0.624, val=0.948]
+Epoch:   2%|         | 23/1000 [00:03<02:39,  6.14it/s, loss=0.62, val=1]     
+Epoch:   2%|         | 24/1000 [00:03<02:41,  6.05it/s, loss=0.62, val=1]
+Epoch:   2%|         | 24/1000 [00:04<02:41,  6.05it/s, loss=0.616, val=1]
+Epoch:   2%|         | 25/1000 [00:04<02:39,  6.12it/s, loss=0.616, val=1]
+Epoch:   2%|         | 25/1000 [00:04<02:39,  6.12it/s, loss=0.613, val=0.997]
+Epoch:   3%|         | 26/1000 [00:04<02:38,  6.15it/s, loss=0.613, val=0.997]
+Epoch:   3%|         | 26/1000 [00:04<02:38,  6.15it/s, loss=0.607, val=0.997]
+Epoch:   3%|         | 27/1000 [00:04<02:38,  6.15it/s, loss=0.607, val=0.997]
+Epoch:   3%|         | 27/1000 [00:04<02:38,  6.15it/s, loss=0.605, val=0.973]
+Epoch:   3%|         | 28/1000 [00:04<02:36,  6.21it/s, loss=0.605, val=0.973]
+Epoch:   3%|         | 28/1000 [00:04<02:36,  6.21it/s, loss=0.602, val=0.963]
+Epoch:   3%|         | 29/1000 [00:04<02:37,  6.16it/s, loss=0.602, val=0.963]
+Epoch:   3%|         | 29/1000 [00:04<02:37,  6.16it/s, loss=0.597, val=0.983]
+Epoch:   3%|         | 30/1000 [00:04<02:36,  6.20it/s, loss=0.597, val=0.983]
+Epoch:   3%|         | 30/1000 [00:05<02:36,  6.20it/s, loss=0.592, val=0.953]
+Epoch:   3%|         | 31/1000 [00:05<02:36,  6.21it/s, loss=0.592, val=0.953]
+Epoch:   3%|         | 31/1000 [00:05<02:36,  6.21it/s, loss=0.593, val=1]    
+Epoch:   3%|         | 32/1000 [00:05<02:37,  6.14it/s, loss=0.593, val=1]
+Epoch:   3%|         | 32/1000 [00:05<02:37,  6.14it/s, loss=0.591, val=0.983]
+Epoch:   3%|         | 33/1000 [00:05<02:38,  6.08it/s, loss=0.591, val=0.983]
+Epoch:   3%|         | 33/1000 [00:05<02:38,  6.08it/s, loss=0.588, val=0.923]
+Epoch:   3%|         | 34/1000 [00:05<02:38,  6.10it/s, loss=0.588, val=0.923]
+Epoch:   3%|         | 34/1000 [00:05<02:38,  6.10it/s, loss=0.586, val=0.99] 
+Epoch:   4%|         | 35/1000 [00:05<02:38,  6.10it/s, loss=0.586, val=0.99]
+Epoch:   4%|         | 35/1000 [00:05<02:38,  6.10it/s, loss=0.583, val=0.95]
+Epoch:   4%|         | 36/1000 [00:05<02:39,  6.05it/s, loss=0.583, val=0.95]
+Epoch:   4%|         | 36/1000 [00:06<02:39,  6.05it/s, loss=0.577, val=0.973]
+Epoch:   4%|         | 37/1000 [00:06<02:36,  6.13it/s, loss=0.577, val=0.973]
+Epoch:   4%|         | 37/1000 [00:06<02:36,  6.13it/s, loss=0.572, val=0.99] 
+Epoch:   4%|         | 38/1000 [00:06<02:37,  6.09it/s, loss=0.572, val=0.99]
+Epoch:   4%|         | 38/1000 [00:06<02:37,  6.09it/s, loss=0.57, val=0.952]
+Epoch:   4%|         | 39/1000 [00:06<02:35,  6.18it/s, loss=0.57, val=0.952]
+Epoch:   4%|         | 39/1000 [00:06<02:35,  6.18it/s, loss=0.568, val=0.987]
+Epoch:   4%|         | 40/1000 [00:06<02:33,  6.24it/s, loss=0.568, val=0.987]
+Epoch:   4%|         | 40/1000 [00:06<02:33,  6.24it/s, loss=0.565, val=0.988]
+Epoch:   4%|         | 41/1000 [00:06<02:32,  6.27it/s, loss=0.565, val=0.988]
+Epoch:   4%|         | 41/1000 [00:06<02:32,  6.27it/s, loss=0.563, val=0.938]
+Epoch:   4%|         | 42/1000 [00:06<02:33,  6.25it/s, loss=0.563, val=0.938]
+Epoch:   4%|         | 42/1000 [00:07<02:33,  6.25it/s, loss=0.561, val=0.87] 
+Epoch:   4%|         | 43/1000 [00:07<02:33,  6.22it/s, loss=0.561, val=0.87]
+Epoch:   4%|         | 43/1000 [00:07<02:33,  6.22it/s, loss=0.559, val=1]   
+Epoch:   4%|         | 44/1000 [00:07<02:40,  5.97it/s, loss=0.559, val=1]
+Epoch:   4%|         | 44/1000 [00:07<02:40,  5.97it/s, loss=0.558, val=0.95]
+Epoch:   4%|         | 45/1000 [00:07<02:38,  6.02it/s, loss=0.558, val=0.95]
+Epoch:   4%|         | 45/1000 [00:07<02:38,  6.02it/s, loss=0.554, val=0.958]
+Epoch:   5%|         | 46/1000 [00:07<02:36,  6.08it/s, loss=0.554, val=0.958]
+Epoch:   5%|         | 46/1000 [00:07<02:36,  6.08it/s, loss=0.551, val=0.917]
+Epoch:   5%|         | 47/1000 [00:07<02:37,  6.06it/s, loss=0.551, val=0.917]
+Epoch:   5%|         | 47/1000 [00:07<02:37,  6.06it/s, loss=0.549, val=0.972]
+Epoch:   5%|         | 48/1000 [00:07<02:36,  6.10it/s, loss=0.549, val=0.972]
+Epoch:   5%|         | 48/1000 [00:08<02:36,  6.10it/s, loss=0.546, val=0.972]
+Epoch:   5%|         | 49/1000 [00:08<02:36,  6.08it/s, loss=0.546, val=0.972]
+Epoch:   5%|         | 49/1000 [00:08<02:36,  6.08it/s, loss=0.543, val=0.87] 
+Epoch:   5%|         | 50/1000 [00:08<02:37,  6.04it/s, loss=0.543, val=0.87]
+Epoch:   5%|         | 50/1000 [00:08<02:37,  6.04it/s, loss=0.54, val=0.963]
+Epoch:   5%|         | 51/1000 [00:08<02:36,  6.07it/s, loss=0.54, val=0.963]
+Epoch:   5%|         | 51/1000 [00:08<02:36,  6.07it/s, loss=0.538, val=0.91]
+Epoch:   5%|         | 52/1000 [00:08<02:36,  6.05it/s, loss=0.538, val=0.91]
+Epoch:   5%|         | 52/1000 [00:08<02:36,  6.05it/s, loss=0.536, val=0.997]
+Epoch:   5%|         | 52/1000 [00:08<02:37,  6.00it/s, loss=0.536, val=0.997]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate[test_plugin0] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.942]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.905]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.847]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.794]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.748]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.718]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.701]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.687]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.687]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.673]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.644]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.625]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.616]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.609]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.602]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.602]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.595]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 74.46it/s, loss=0.592]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.592]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.581]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.573]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.564]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.557]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.551]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.546]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.541]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.536]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 83.07it/s, loss=0.53] 
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.53]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.524]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.521]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.52] 
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.515]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.512]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.512]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.509]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.504]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 85.26it/s, loss=0.501]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.501]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.498]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.494]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.492]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.491]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.49] 
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.489]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.489]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.488]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 85.37it/s, loss=0.483]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.483]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.48] 
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.478]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.475]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.473]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.471]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.469]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.468]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.467]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 86.16it/s, loss=0.466]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.466]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.463]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.463]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.46] 
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.459]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.457]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.456]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.455]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.453]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.453]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.15it/s, loss=0.452]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.452]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.45] 
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.449]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.448]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.447]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.445]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.443]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.441]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.439]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.75it/s, loss=0.437]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.437]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.436]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.434]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.433]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.432]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.432]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.431]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.431]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.431]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 87.62it/s, loss=0.431]
+Epoch:   8%|         | 81/1000 [00:00<00:11, 83.15it/s, loss=0.431]
+Epoch:   8%|         | 81/1000 [00:00<00:11, 83.15it/s, loss=0.43] 
+Epoch:   8%|         | 81/1000 [00:00<00:11, 83.15it/s, loss=0.429]
+Epoch:   8%|         | 81/1000 [00:00<00:11, 83.15it/s, loss=0.428]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 83.15it/s, loss=0.427]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 83.15it/s, loss=0.426]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 83.15it/s, loss=0.426]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 83.15it/s, loss=0.426]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 83.15it/s, loss=0.425]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 83.15it/s, loss=0.424]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.424]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.422]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.421]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.42] 
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.42]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.418]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.418]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.416]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.416]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 84.23it/s, loss=0.415]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.415]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.415]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.386]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.401]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.374]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.363]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.363]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.36] 
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.356]
+Epoch:  10%|         | 99/1000 [00:01<00:10, 82.40it/s, loss=0.356]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.356]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.356]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.355]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.355]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.355]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.349]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.351]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.351]
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.35] 
+Epoch:  11%|         | 108/1000 [00:01<00:10, 84.17it/s, loss=0.348]
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.348]
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.349]
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.349]
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.347]
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.347]
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.35] 
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.348]
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.35] 
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.348]
+Epoch:  12%|        | 117/1000 [00:01<00:10, 84.35it/s, loss=0.347]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.347]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.347]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.347]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.346]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.346]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.345]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.346]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.346]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.347]
+Epoch:  13%|        | 126/1000 [00:01<00:10, 84.63it/s, loss=0.349]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.349]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.348]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.346]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.348]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.347]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.345]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.346]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.345]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.346]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 84.63it/s, loss=0.347]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.347]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.348]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.347]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.346]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.346]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.346]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.345]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.344]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.343]
+Epoch:  14%|        | 144/1000 [00:01<00:09, 85.90it/s, loss=0.344]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.344]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.345]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.345]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.344]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.344]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.346]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.345]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.345]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.344]
+Epoch:  15%|        | 153/1000 [00:01<00:10, 84.28it/s, loss=0.344]
+Epoch:  16%|        | 162/1000 [00:01<00:10, 77.68it/s, loss=0.344]
+Epoch:  16%|        | 162/1000 [00:01<00:10, 77.68it/s, loss=0.344]
+Epoch:  16%|        | 162/1000 [00:01<00:10, 77.68it/s, loss=0.343]
+Epoch:  16%|        | 162/1000 [00:01<00:10, 77.68it/s, loss=0.343]
+Epoch:  16%|        | 162/1000 [00:01<00:10, 77.68it/s, loss=0.342]
+Epoch:  16%|        | 162/1000 [00:02<00:10, 77.68it/s, loss=0.343]
+Epoch:  16%|        | 162/1000 [00:02<00:10, 77.68it/s, loss=0.342]
+Epoch:  16%|        | 162/1000 [00:02<00:10, 77.68it/s, loss=0.342]
+Epoch:  16%|        | 162/1000 [00:02<00:10, 77.68it/s, loss=0.342]
+Epoch:  16%|        | 162/1000 [00:02<00:10, 77.68it/s, loss=0.341]
+Epoch:  16%|        | 162/1000 [00:02<00:10, 77.68it/s, loss=0.341]
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.341]
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.341]
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.34] 
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.34]
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.34]
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.339]
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.34] 
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.339]
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.34] 
+Epoch:  17%|        | 172/1000 [00:02<00:10, 80.89it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.339]
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.34] 
+Epoch:  18%|        | 181/1000 [00:02<00:10, 79.87it/s, loss=0.341]
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+Epoch:  19%|        | 190/1000 [00:02<00:10, 79.34it/s, loss=0.338]
+Epoch:  19%|        | 190/1000 [00:02<00:10, 79.34it/s, loss=0.338]
+Epoch:  19%|        | 190/1000 [00:02<00:10, 79.34it/s, loss=0.338]
+Epoch:  19%|        | 190/1000 [00:02<00:10, 79.34it/s, loss=0.339]
+Epoch:  19%|        | 190/1000 [00:02<00:10, 79.34it/s, loss=0.339]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.77it/s, loss=0.339]
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+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.77it/s, loss=0.324]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.77it/s, loss=0.329]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.77it/s, loss=0.329]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.77it/s, loss=0.334]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 82.77it/s, loss=0.341]
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+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.349]
+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.353]
+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.354]
+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.358]
+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.357]
+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.356]
+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.359]
+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.36] 
+Epoch:  21%|        | 210/1000 [00:02<00:09, 84.94it/s, loss=0.358]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.358]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.361]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.36] 
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.361]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.359]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.361]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.361]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.359]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.357]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.19it/s, loss=0.358]
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+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.23it/s, loss=0.341]
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+Epoch:  28%|       | 284/1000 [00:03<00:08, 80.88it/s, loss=0.337]
+Epoch:  28%|       | 284/1000 [00:03<00:08, 80.88it/s, loss=0.337]
+Epoch:  28%|       | 284/1000 [00:03<00:08, 80.88it/s, loss=0.336]
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+Epoch:  94%|| 944/1000 [00:11<00:00, 77.34it/s, loss=0.308]
+Epoch:  94%|| 944/1000 [00:11<00:00, 77.34it/s, loss=0.308]
+Epoch:  94%|| 944/1000 [00:11<00:00, 77.34it/s, loss=0.307]
+Epoch:  94%|| 944/1000 [00:11<00:00, 77.34it/s, loss=0.307]
+Epoch:  94%|| 944/1000 [00:11<00:00, 77.34it/s, loss=0.307]
+Epoch:  94%|| 944/1000 [00:11<00:00, 77.34it/s, loss=0.308]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.308]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.308]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.308]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.308]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.308]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.307]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.307]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.307]
+Epoch:  95%|| 952/1000 [00:11<00:00, 76.61it/s, loss=0.307]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.307]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.307]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.307]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.306]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.306]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.306]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.306]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.306]
+Epoch:  96%|| 960/1000 [00:11<00:00, 77.03it/s, loss=0.306]
+Epoch:  97%|| 968/1000 [00:11<00:00, 74.83it/s, loss=0.306]
+Epoch:  97%|| 968/1000 [00:11<00:00, 74.83it/s, loss=0.306]
+Epoch:  97%|| 968/1000 [00:12<00:00, 74.83it/s, loss=0.306]
+Epoch:  97%|| 968/1000 [00:12<00:00, 74.83it/s, loss=0.306]
+Epoch:  97%|| 968/1000 [00:12<00:00, 74.83it/s, loss=0.305]
+Epoch:  97%|| 968/1000 [00:12<00:00, 74.83it/s, loss=0.306]
+Epoch:  97%|| 968/1000 [00:12<00:00, 74.83it/s, loss=0.307]
+Epoch:  97%|| 968/1000 [00:12<00:00, 74.83it/s, loss=0.306]
+Epoch:  97%|| 968/1000 [00:12<00:00, 74.83it/s, loss=0.306]
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.306]
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.306]
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.307]
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.307]
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.308]
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.308]
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.308]
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.31] 
+Epoch:  98%|| 976/1000 [00:12<00:00, 75.69it/s, loss=0.311]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.311]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31] 
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31]
+Epoch:  98%|| 984/1000 [00:12<00:00, 76.26it/s, loss=0.31]
+Epoch:  99%|| 993/1000 [00:12<00:00, 80.14it/s, loss=0.31]
+Epoch:  99%|| 993/1000 [00:12<00:00, 80.14it/s, loss=0.31]
+Epoch:  99%|| 993/1000 [00:12<00:00, 80.14it/s, loss=0.309]
+Epoch:  99%|| 993/1000 [00:12<00:00, 80.14it/s, loss=0.31] 
+Epoch:  99%|| 993/1000 [00:12<00:00, 80.14it/s, loss=0.31]
+Epoch:  99%|| 993/1000 [00:12<00:00, 80.14it/s, loss=0.309]
+Epoch:  99%|| 993/1000 [00:12<00:00, 80.14it/s, loss=0.308]
+Epoch:  99%|| 993/1000 [00:12<00:00, 80.14it/s, loss=0.309]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 80.84it/s, loss=0.309]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate[test_plugin1] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.942]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.905]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.847]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.794]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.748]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.718]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.701]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.687]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.687]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.673]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.644]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.625]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.616]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.609]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.602]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.602]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.595]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 76.79it/s, loss=0.592]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.592]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.581]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.573]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.564]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.557]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.551]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.546]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.541]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.536]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 81.94it/s, loss=0.53] 
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.53]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.524]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.521]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.52] 
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.515]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.512]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.512]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.509]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.504]
+Epoch:   3%|         | 26/1000 [00:00<00:11, 83.24it/s, loss=0.501]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.501]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.498]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.494]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.492]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.491]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.49] 
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.489]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.489]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.488]
+Epoch:   4%|         | 35/1000 [00:00<00:11, 80.97it/s, loss=0.483]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.483]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.48] 
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.478]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.475]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.473]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.471]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.469]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.468]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.467]
+Epoch:   4%|         | 44/1000 [00:00<00:11, 81.00it/s, loss=0.466]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.466]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.463]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.463]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.46] 
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.459]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.457]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.456]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.455]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.453]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 82.20it/s, loss=0.453]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.453]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.452]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.45] 
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.449]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.448]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.447]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.445]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.443]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.441]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.439]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.25it/s, loss=0.437]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.437]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.436]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.434]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.433]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.432]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.432]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.431]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.431]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.431]
+Epoch:   7%|         | 72/1000 [00:00<00:10, 86.09it/s, loss=0.431]
+Epoch:   8%|         | 81/1000 [00:00<00:11, 81.81it/s, loss=0.431]
+Epoch:   8%|         | 81/1000 [00:00<00:11, 81.81it/s, loss=0.43] 
+Epoch:   8%|         | 81/1000 [00:01<00:11, 81.81it/s, loss=0.429]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 81.81it/s, loss=0.428]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 81.81it/s, loss=0.427]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 81.81it/s, loss=0.426]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 81.81it/s, loss=0.426]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 81.81it/s, loss=0.426]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 81.81it/s, loss=0.425]
+Epoch:   8%|         | 81/1000 [00:01<00:11, 81.81it/s, loss=0.424]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.424]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.422]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.421]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.42] 
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.42]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.418]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.418]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.416]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.416]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.415]
+Epoch:   9%|         | 90/1000 [00:01<00:10, 83.30it/s, loss=0.415]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.415]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.386]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.401]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.374]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.363]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.363]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.36] 
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.356]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.356]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 85.53it/s, loss=0.356]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.356]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.355]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.355]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.355]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.349]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.351]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.351]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.35] 
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.348]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 83.62it/s, loss=0.349]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.349]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.349]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.347]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.347]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.35] 
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.348]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.35] 
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.348]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.347]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 83.50it/s, loss=0.347]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.347]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.347]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.346]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.346]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.345]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.346]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.346]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.347]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.349]
+Epoch:  13%|        | 127/1000 [00:01<00:10, 83.33it/s, loss=0.348]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.348]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.346]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.348]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.347]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.345]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.346]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.345]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.346]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.347]
+Epoch:  14%|        | 136/1000 [00:01<00:10, 84.62it/s, loss=0.348]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.348]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.347]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.346]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.346]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.346]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.345]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.344]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.343]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.344]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.345]
+Epoch:  14%|        | 145/1000 [00:01<00:09, 85.55it/s, loss=0.345]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.345]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.346]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.345]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.345]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.344]
+Epoch:  16%|        | 155/1000 [00:01<00:09, 87.20it/s, loss=0.343]
+Epoch:  16%|        | 164/1000 [00:01<00:09, 87.15it/s, loss=0.343]
+Epoch:  16%|        | 164/1000 [00:01<00:09, 87.15it/s, loss=0.343]
+Epoch:  16%|        | 164/1000 [00:01<00:09, 87.15it/s, loss=0.342]
+Epoch:  16%|        | 164/1000 [00:01<00:09, 87.15it/s, loss=0.343]
+Epoch:  16%|        | 164/1000 [00:01<00:09, 87.15it/s, loss=0.342]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 87.15it/s, loss=0.342]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 87.15it/s, loss=0.342]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 87.15it/s, loss=0.341]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 87.15it/s, loss=0.341]
+Epoch:  16%|        | 164/1000 [00:02<00:09, 87.15it/s, loss=0.341]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.341]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.34] 
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.34]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.34]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.339]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.34] 
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.339]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.34] 
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.339]
+Epoch:  17%|        | 173/1000 [00:02<00:10, 82.03it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.339]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.34] 
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.341]
+Epoch:  18%|        | 182/1000 [00:02<00:09, 83.81it/s, loss=0.34] 
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.34]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.34]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.339]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.339]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.339]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.338]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.338]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.338]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.339]
+Epoch:  19%|        | 191/1000 [00:02<00:09, 85.53it/s, loss=0.339]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.339]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.345]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.298]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.318]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.324]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.329]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.329]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.334]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.341]
+Epoch:  20%|        | 200/1000 [00:02<00:09, 85.66it/s, loss=0.34] 
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.34]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.343]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.348]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.349]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.353]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.354]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.358]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.357]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.356]
+Epoch:  21%|        | 209/1000 [00:02<00:09, 86.34it/s, loss=0.359]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.359]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.36] 
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.358]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.361]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.36] 
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.361]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.359]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.361]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.361]
+Epoch:  22%|       | 218/1000 [00:02<00:09, 84.06it/s, loss=0.359]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.359]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.357]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.358]
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+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.355]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.354]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.355]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.353]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.352]
+Epoch:  23%|       | 227/1000 [00:02<00:09, 82.61it/s, loss=0.351]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.351]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.348]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.347]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.347]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.347]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.346]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.346]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.346]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.346]
+Epoch:  24%|       | 236/1000 [00:02<00:09, 79.85it/s, loss=0.346]
+Epoch:  24%|       | 245/1000 [00:02<00:09, 82.41it/s, loss=0.346]
+Epoch:  24%|       | 245/1000 [00:02<00:09, 82.41it/s, loss=0.346]
+Epoch:  24%|       | 245/1000 [00:02<00:09, 82.41it/s, loss=0.345]
+Epoch:  24%|       | 245/1000 [00:02<00:09, 82.41it/s, loss=0.345]
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+Epoch:  24%|       | 245/1000 [00:02<00:09, 82.41it/s, loss=0.345]
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+Epoch:  24%|       | 245/1000 [00:03<00:09, 82.41it/s, loss=0.343]
+Epoch:  24%|       | 245/1000 [00:03<00:09, 82.41it/s, loss=0.342]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.342]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.344]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.343]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.342]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.342]
+Epoch:  25%|       | 254/1000 [00:03<00:09, 79.76it/s, loss=0.342]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.342]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.342]
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+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.343]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.343]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.342]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.342]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.343]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.342]
+Epoch:  26%|       | 263/1000 [00:03<00:09, 81.82it/s, loss=0.342]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.21it/s, loss=0.342]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.21it/s, loss=0.342]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.21it/s, loss=0.341]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.21it/s, loss=0.341]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.21it/s, loss=0.341]
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+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.21it/s, loss=0.338]
+Epoch:  27%|       | 272/1000 [00:03<00:09, 79.21it/s, loss=0.338]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.338]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.338]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.338]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.338]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.338]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.337]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.337]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.337]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.337]
+Epoch:  28%|       | 282/1000 [00:03<00:08, 82.69it/s, loss=0.337]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.337]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.337]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.336]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.336]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.336]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.336]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.336]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.335]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.335]
+Epoch:  29%|       | 291/1000 [00:03<00:08, 79.84it/s, loss=0.335]
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+Epoch:  30%|       | 300/1000 [00:03<00:09, 77.39it/s, loss=0.305]
+Epoch:  30%|       | 300/1000 [00:03<00:09, 77.39it/s, loss=0.3]  
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+Epoch:  30%|       | 300/1000 [00:03<00:09, 77.39it/s, loss=0.315]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 80.21it/s, loss=0.315]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 80.21it/s, loss=0.31] 
+Epoch:  31%|       | 309/1000 [00:03<00:08, 80.21it/s, loss=0.307]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 80.21it/s, loss=0.305]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 80.21it/s, loss=0.31] 
+Epoch:  31%|       | 309/1000 [00:03<00:08, 80.21it/s, loss=0.311]
+Epoch:  31%|       | 309/1000 [00:03<00:08, 80.21it/s, loss=0.312]
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+Epoch:  31%|       | 309/1000 [00:03<00:08, 80.21it/s, loss=0.314]
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+Epoch:  32%|      | 318/1000 [00:03<00:08, 80.39it/s, loss=0.315]
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+Epoch:  32%|      | 318/1000 [00:03<00:08, 80.39it/s, loss=0.315]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 80.39it/s, loss=0.317]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 80.39it/s, loss=0.316]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 80.39it/s, loss=0.315]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 80.39it/s, loss=0.315]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 80.39it/s, loss=0.312]
+Epoch:  32%|      | 318/1000 [00:03<00:08, 80.39it/s, loss=0.311]
+Epoch:  33%|      | 327/1000 [00:03<00:08, 81.12it/s, loss=0.311]
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+Epoch:  33%|      | 327/1000 [00:03<00:08, 81.12it/s, loss=0.311]
+Epoch:  33%|      | 327/1000 [00:03<00:08, 81.12it/s, loss=0.311]
+Epoch:  33%|      | 327/1000 [00:03<00:08, 81.12it/s, loss=0.312]
+Epoch:  33%|      | 327/1000 [00:04<00:08, 81.12it/s, loss=0.312]
+Epoch:  33%|      | 327/1000 [00:04<00:08, 81.12it/s, loss=0.313]
+Epoch:  33%|      | 327/1000 [00:04<00:08, 81.12it/s, loss=0.315]
+Epoch:  33%|      | 327/1000 [00:04<00:08, 81.12it/s, loss=0.313]
+Epoch:  33%|      | 327/1000 [00:04<00:08, 81.12it/s, loss=0.314]
+Epoch:  34%|      | 336/1000 [00:04<00:07, 83.47it/s, loss=0.314]
+Epoch:  34%|      | 336/1000 [00:04<00:07, 83.47it/s, loss=0.314]
+Epoch:  34%|      | 336/1000 [00:04<00:07, 83.47it/s, loss=0.314]
+Epoch:  34%|      | 336/1000 [00:04<00:07, 83.47it/s, loss=0.317]
+Epoch:  34%|      | 336/1000 [00:04<00:07, 83.47it/s, loss=0.317]
+Epoch:  34%|      | 336/1000 [00:04<00:07, 83.47it/s, loss=0.319]
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+Epoch:  34%|      | 336/1000 [00:04<00:07, 83.47it/s, loss=0.32]
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+Epoch:  60%|    | 599/1000 [00:07<00:04, 82.41it/s, loss=0.326]
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+Epoch:  92%|| 925/1000 [00:11<00:00, 75.65it/s, loss=0.308]
+Epoch:  92%|| 925/1000 [00:11<00:00, 75.65it/s, loss=0.309]
+Epoch:  92%|| 925/1000 [00:11<00:00, 75.65it/s, loss=0.31] 
+Epoch:  92%|| 925/1000 [00:11<00:00, 75.65it/s, loss=0.309]
+Epoch:  92%|| 925/1000 [00:11<00:00, 75.65it/s, loss=0.31] 
+Epoch:  92%|| 925/1000 [00:11<00:00, 75.65it/s, loss=0.311]
+Epoch:  92%|| 925/1000 [00:11<00:00, 75.65it/s, loss=0.312]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.312]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.312]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.312]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.311]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.311]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.313]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.313]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.311]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.311]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.26it/s, loss=0.311]
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.311]
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.31] 
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.31]
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.308]
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.309]
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.308]
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.308]
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.307]
+Epoch:  94%|| 942/1000 [00:11<00:00, 77.52it/s, loss=0.307]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.307]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.307]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.308]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.308]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.308]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.308]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.308]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.307]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.307]
+Epoch:  95%|| 950/1000 [00:11<00:00, 78.18it/s, loss=0.307]
+Epoch:  96%|| 959/1000 [00:11<00:00, 79.74it/s, loss=0.307]
+Epoch:  96%|| 959/1000 [00:11<00:00, 79.74it/s, loss=0.307]
+Epoch:  96%|| 959/1000 [00:11<00:00, 79.74it/s, loss=0.307]
+Epoch:  96%|| 959/1000 [00:11<00:00, 79.74it/s, loss=0.307]
+Epoch:  96%|| 959/1000 [00:11<00:00, 79.74it/s, loss=0.306]
+Epoch:  96%|| 959/1000 [00:11<00:00, 79.74it/s, loss=0.306]
+Epoch:  96%|| 959/1000 [00:11<00:00, 79.74it/s, loss=0.306]
+Epoch:  96%|| 959/1000 [00:11<00:00, 79.74it/s, loss=0.306]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.74it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.305]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.307]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.306]
+Epoch:  97%|| 967/1000 [00:12<00:00, 78.74it/s, loss=0.306]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.306]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.306]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.307]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.307]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.308]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.308]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.308]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.31] 
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.311]
+Epoch:  98%|| 976/1000 [00:12<00:00, 79.52it/s, loss=0.31] 
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  98%|| 985/1000 [00:12<00:00, 80.61it/s, loss=0.31]
+Epoch:  99%|| 994/1000 [00:12<00:00, 77.86it/s, loss=0.31]
+Epoch:  99%|| 994/1000 [00:12<00:00, 77.86it/s, loss=0.309]
+Epoch:  99%|| 994/1000 [00:12<00:00, 77.86it/s, loss=0.31] 
+Epoch:  99%|| 994/1000 [00:12<00:00, 77.86it/s, loss=0.31]
+Epoch:  99%|| 994/1000 [00:12<00:00, 77.86it/s, loss=0.309]
+Epoch:  99%|| 994/1000 [00:12<00:00, 77.86it/s, loss=0.308]
+Epoch:  99%|| 994/1000 [00:12<00:00, 77.86it/s, loss=0.309]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 80.48it/s, loss=0.309]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate[test_plugin2] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.942]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.905]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.847]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.794]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.748]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.718]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.701]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.687]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.687]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.673]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.644]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.625]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.616]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.609]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.602]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.602]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 75.74it/s, loss=0.595]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.595]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.592]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.581]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.573]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.564]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.557]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.551]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.546]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.541]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 77.16it/s, loss=0.536]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.536]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.53] 
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.524]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.521]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.52] 
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.515]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.512]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.512]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 77.62it/s, loss=0.509]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.509]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.504]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.501]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.498]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.494]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.492]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.491]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.49] 
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.489]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.489]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 78.14it/s, loss=0.488]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.488]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.483]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.48] 
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.478]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.475]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.473]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.471]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.469]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.468]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.467]
+Epoch:   4%|         | 43/1000 [00:00<00:11, 82.80it/s, loss=0.466]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.466]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.463]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.463]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.46] 
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.459]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.457]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.456]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.455]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.453]
+Epoch:   5%|         | 53/1000 [00:00<00:11, 85.44it/s, loss=0.453]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.453]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.452]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.45] 
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.449]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.448]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.447]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.445]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.443]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.441]
+Epoch:   6%|         | 62/1000 [00:00<00:11, 84.91it/s, loss=0.439]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.439]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.437]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.436]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.434]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.433]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.432]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.432]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.431]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.431]
+Epoch:   7%|         | 71/1000 [00:00<00:11, 83.60it/s, loss=0.431]
+Epoch:   8%|         | 80/1000 [00:00<00:11, 79.79it/s, loss=0.431]
+Epoch:   8%|         | 80/1000 [00:00<00:11, 79.79it/s, loss=0.431]
+Epoch:   8%|         | 80/1000 [00:01<00:11, 79.79it/s, loss=0.43] 
+Epoch:   8%|         | 80/1000 [00:01<00:11, 79.79it/s, loss=0.429]
+Epoch:   8%|         | 80/1000 [00:01<00:11, 79.79it/s, loss=0.428]
+Epoch:   8%|         | 80/1000 [00:01<00:11, 79.79it/s, loss=0.427]
+Epoch:   8%|         | 80/1000 [00:01<00:11, 79.79it/s, loss=0.426]
+Epoch:   8%|         | 80/1000 [00:01<00:11, 79.79it/s, loss=0.426]
+Epoch:   8%|         | 80/1000 [00:01<00:11, 79.79it/s, loss=0.426]
+Epoch:   8%|         | 80/1000 [00:01<00:11, 79.79it/s, loss=0.425]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.425]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.424]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.422]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.421]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.42] 
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.42]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.418]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.418]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.416]
+Epoch:   9%|         | 89/1000 [00:01<00:11, 82.50it/s, loss=0.416]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.416]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.415]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.415]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.386]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.401]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.374]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.363]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.363]
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.36] 
+Epoch:  10%|         | 98/1000 [00:01<00:10, 82.49it/s, loss=0.356]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.356]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.356]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.356]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.355]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.355]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.355]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.349]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.351]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.351]
+Epoch:  11%|         | 107/1000 [00:01<00:10, 84.56it/s, loss=0.35] 
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.35]
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.348]
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.349]
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.349]
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.347]
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.347]
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.35] 
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.348]
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.35] 
+Epoch:  12%|        | 116/1000 [00:01<00:10, 82.29it/s, loss=0.348]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 83.91it/s, loss=0.348]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 83.91it/s, loss=0.347]
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+Epoch:  14%|        | 143/1000 [00:01<00:10, 80.76it/s, loss=0.346]
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+Epoch:  14%|        | 143/1000 [00:01<00:10, 80.76it/s, loss=0.344]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 80.76it/s, loss=0.343]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.343]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.344]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.345]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.345]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.344]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.344]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.346]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.345]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.345]
+Epoch:  15%|        | 152/1000 [00:01<00:10, 81.45it/s, loss=0.344]
+Epoch:  16%|        | 161/1000 [00:01<00:10, 82.27it/s, loss=0.344]
+Epoch:  16%|        | 161/1000 [00:01<00:10, 82.27it/s, loss=0.344]
+Epoch:  16%|        | 161/1000 [00:01<00:10, 82.27it/s, loss=0.344]
+Epoch:  16%|        | 161/1000 [00:02<00:10, 82.27it/s, loss=0.343]
+Epoch:  16%|        | 161/1000 [00:02<00:10, 82.27it/s, loss=0.343]
+Epoch:  16%|        | 161/1000 [00:02<00:10, 82.27it/s, loss=0.342]
+Epoch:  16%|        | 161/1000 [00:02<00:10, 82.27it/s, loss=0.343]
+Epoch:  16%|        | 161/1000 [00:02<00:10, 82.27it/s, loss=0.342]
+Epoch:  16%|        | 161/1000 [00:02<00:10, 82.27it/s, loss=0.342]
+Epoch:  16%|        | 161/1000 [00:02<00:10, 82.27it/s, loss=0.342]
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.342]
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.341]
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.341]
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.341]
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.34] 
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.34]
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.34]
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.339]
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.34] 
+Epoch:  17%|        | 170/1000 [00:02<00:10, 80.07it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.34] 
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 80.35it/s, loss=0.339]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.339]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.34] 
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.341]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.34] 
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.34]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.339]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.339]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.339]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.338]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.338]
+Epoch:  19%|        | 188/1000 [00:02<00:09, 81.34it/s, loss=0.338]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.338]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.339]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.339]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.345]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.298]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.318]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.324]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.329]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.329]
+Epoch:  20%|        | 198/1000 [00:02<00:09, 84.89it/s, loss=0.334]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.334]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.341]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.34] 
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.343]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.348]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.349]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.353]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.354]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.358]
+Epoch:  21%|        | 207/1000 [00:02<00:09, 83.97it/s, loss=0.357]
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.357]
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.356]
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.359]
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.36] 
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.358]
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.361]
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.36] 
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.361]
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.359]
+Epoch:  22%|       | 216/1000 [00:02<00:09, 84.03it/s, loss=0.361]
+Epoch:  22%|       | 225/1000 [00:02<00:09, 84.19it/s, loss=0.361]
+Epoch:  22%|       | 225/1000 [00:02<00:09, 84.19it/s, loss=0.361]
+Epoch:  22%|       | 225/1000 [00:02<00:09, 84.19it/s, loss=0.359]
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+Epoch:  22%|       | 225/1000 [00:02<00:09, 84.19it/s, loss=0.355]
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+Epoch:  23%|       | 234/1000 [00:02<00:09, 83.63it/s, loss=0.352]
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+Epoch:  24%|       | 243/1000 [00:02<00:09, 79.57it/s, loss=0.346]
+Epoch:  24%|       | 243/1000 [00:02<00:09, 79.57it/s, loss=0.346]
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+Epoch:  25%|       | 252/1000 [00:03<00:09, 79.27it/s, loss=0.342]
+Epoch:  25%|       | 252/1000 [00:03<00:09, 79.27it/s, loss=0.343]
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+Epoch:  26%|       | 261/1000 [00:03<00:08, 82.19it/s, loss=0.342]
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+Epoch:  29%|       | 288/1000 [00:03<00:09, 78.99it/s, loss=0.336]
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+Epoch:  30%|       | 297/1000 [00:03<00:08, 81.60it/s, loss=0.3]  
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+Epoch:  31%|       | 306/1000 [00:03<00:08, 80.24it/s, loss=0.315]
+Epoch:  31%|       | 306/1000 [00:03<00:08, 80.24it/s, loss=0.31] 
+Epoch:  31%|       | 306/1000 [00:03<00:08, 80.24it/s, loss=0.307]
+Epoch:  31%|       | 306/1000 [00:03<00:08, 80.24it/s, loss=0.305]
+Epoch:  31%|       | 306/1000 [00:03<00:08, 80.24it/s, loss=0.31] 
+Epoch:  31%|       | 306/1000 [00:03<00:08, 80.24it/s, loss=0.311]
+Epoch:  31%|       | 306/1000 [00:03<00:08, 80.24it/s, loss=0.312]
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+Epoch:  32%|      | 315/1000 [00:03<00:08, 81.34it/s, loss=0.315]
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+Epoch:  32%|      | 315/1000 [00:03<00:08, 81.34it/s, loss=0.316]
+Epoch:  32%|      | 315/1000 [00:03<00:08, 81.34it/s, loss=0.315]
+Epoch:  32%|      | 324/1000 [00:03<00:08, 82.17it/s, loss=0.315]
+Epoch:  32%|      | 324/1000 [00:03<00:08, 82.17it/s, loss=0.315]
+Epoch:  32%|      | 324/1000 [00:03<00:08, 82.17it/s, loss=0.312]
+Epoch:  32%|      | 324/1000 [00:03<00:08, 82.17it/s, loss=0.311]
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+Epoch:  32%|      | 324/1000 [00:04<00:08, 82.17it/s, loss=0.312]
+Epoch:  32%|      | 324/1000 [00:04<00:08, 82.17it/s, loss=0.313]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.313]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.315]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.313]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.314]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.314]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.314]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.317]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.317]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.319]
+Epoch:  33%|      | 333/1000 [00:04<00:08, 81.17it/s, loss=0.32] 
+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.32]
+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.32]
+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.319]
+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.321]
+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.322]
+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.323]
+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.322]
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+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.325]
+Epoch:  34%|      | 342/1000 [00:04<00:08, 81.66it/s, loss=0.327]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.327]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.327]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.328]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.329]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.33] 
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+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.331]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.331]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.331]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 82.12it/s, loss=0.331]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 82.92it/s, loss=0.331]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 82.92it/s, loss=0.331]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 82.92it/s, loss=0.33] 
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+Epoch:  36%|      | 360/1000 [00:04<00:07, 82.92it/s, loss=0.329]
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+Epoch:  37%|      | 369/1000 [00:04<00:08, 78.82it/s, loss=0.329]
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+Epoch:  37%|      | 369/1000 [00:04<00:08, 78.82it/s, loss=0.329]
+Epoch:  37%|      | 369/1000 [00:04<00:08, 78.82it/s, loss=0.329]
+Epoch:  37%|      | 369/1000 [00:04<00:08, 78.82it/s, loss=0.329]
+Epoch:  38%|      | 377/1000 [00:04<00:07, 78.71it/s, loss=0.329]
+Epoch:  38%|      | 377/1000 [00:04<00:07, 78.71it/s, loss=0.329]
+Epoch:  38%|      | 377/1000 [00:04<00:07, 78.71it/s, loss=0.329]
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+Epoch:  38%|      | 377/1000 [00:04<00:07, 78.71it/s, loss=0.331]
+Epoch:  38%|      | 377/1000 [00:04<00:07, 78.71it/s, loss=0.331]
+Epoch:  38%|      | 377/1000 [00:04<00:07, 78.71it/s, loss=0.331]
+Epoch:  38%|      | 377/1000 [00:04<00:07, 78.71it/s, loss=0.331]
+Epoch:  38%|      | 377/1000 [00:04<00:07, 78.71it/s, loss=0.331]
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+Epoch:  39%|      | 386/1000 [00:04<00:07, 79.45it/s, loss=0.331]
+Epoch:  39%|      | 386/1000 [00:04<00:07, 79.45it/s, loss=0.331]
+Epoch:  39%|      | 386/1000 [00:04<00:07, 79.45it/s, loss=0.331]
+Epoch:  39%|      | 386/1000 [00:04<00:07, 79.45it/s, loss=0.331]
+Epoch:  39%|      | 386/1000 [00:04<00:07, 79.45it/s, loss=0.332]
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+Epoch:  39%|      | 386/1000 [00:04<00:07, 79.45it/s, loss=0.332]
+Epoch:  39%|      | 386/1000 [00:04<00:07, 79.45it/s, loss=0.332]
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+Epoch:  40%|      | 395/1000 [00:04<00:07, 81.27it/s, loss=0.333]
+Epoch:  40%|      | 395/1000 [00:04<00:07, 81.27it/s, loss=0.333]
+Epoch:  40%|      | 395/1000 [00:04<00:07, 81.27it/s, loss=0.333]
+Epoch:  40%|      | 395/1000 [00:04<00:07, 81.27it/s, loss=0.333]
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+Epoch:  53%|    | 529/1000 [00:06<00:05, 79.96it/s, loss=0.334]
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+Epoch:  90%| | 899/1000 [00:11<00:01, 80.36it/s, loss=0.314]
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.314]
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.31] 
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.309]
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.312]
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.31] 
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.31]
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.311]
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.31] 
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.31]
+Epoch:  91%| | 908/1000 [00:11<00:01, 80.46it/s, loss=0.306]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.306]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.308]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.304]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.302]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.302]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.305]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.309]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.309]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.309]
+Epoch:  92%|| 917/1000 [00:11<00:01, 79.83it/s, loss=0.307]
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.307]
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.308]
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.309]
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.31] 
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.309]
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.31] 
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.311]
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.312]
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.312]
+Epoch:  93%|| 926/1000 [00:11<00:00, 80.89it/s, loss=0.312]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.312]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.311]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.311]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.313]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.313]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.311]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.311]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.311]
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.31] 
+Epoch:  94%|| 935/1000 [00:11<00:00, 77.55it/s, loss=0.31]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.31]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.308]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.309]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.308]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.308]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.307]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.307]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.307]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.308]
+Epoch:  94%|| 944/1000 [00:11<00:00, 79.24it/s, loss=0.308]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.308]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.308]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.308]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.308]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.307]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.307]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.307]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.307]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.307]
+Epoch:  95%|| 953/1000 [00:11<00:00, 80.92it/s, loss=0.307]
+Epoch:  96%|| 962/1000 [00:11<00:00, 80.40it/s, loss=0.307]
+Epoch:  96%|| 962/1000 [00:11<00:00, 80.40it/s, loss=0.306]
+Epoch:  96%|| 962/1000 [00:11<00:00, 80.40it/s, loss=0.306]
+Epoch:  96%|| 962/1000 [00:11<00:00, 80.40it/s, loss=0.306]
+Epoch:  96%|| 962/1000 [00:11<00:00, 80.40it/s, loss=0.306]
+Epoch:  96%|| 962/1000 [00:12<00:00, 80.40it/s, loss=0.306]
+Epoch:  96%|| 962/1000 [00:12<00:00, 80.40it/s, loss=0.306]
+Epoch:  96%|| 962/1000 [00:12<00:00, 80.40it/s, loss=0.306]
+Epoch:  96%|| 962/1000 [00:12<00:00, 80.40it/s, loss=0.306]
+Epoch:  96%|| 962/1000 [00:12<00:00, 80.40it/s, loss=0.306]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.306]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.305]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.306]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.307]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.306]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.306]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.306]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.307]
+Epoch:  97%|| 971/1000 [00:12<00:00, 76.71it/s, loss=0.307]
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.307]
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.308]
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.308]
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.308]
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.31] 
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.311]
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.31] 
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.31]
+Epoch:  98%|| 979/1000 [00:12<00:00, 74.97it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.309]
+Epoch:  99%|| 987/1000 [00:12<00:00, 75.27it/s, loss=0.31] 
+Epoch: 100%|| 996/1000 [00:12<00:00, 78.35it/s, loss=0.31]
+Epoch: 100%|| 996/1000 [00:12<00:00, 78.35it/s, loss=0.31]
+Epoch: 100%|| 996/1000 [00:12<00:00, 78.35it/s, loss=0.309]
+Epoch: 100%|| 996/1000 [00:12<00:00, 78.35it/s, loss=0.308]
+Epoch: 100%|| 996/1000 [00:12<00:00, 78.35it/s, loss=0.309]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 80.49it/s, loss=0.309]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate[test_plugin3] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.924]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.917]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.876]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.829]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.805]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.784]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.784]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.769]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.75] 
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.734]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.722]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.71] 
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.703]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.693]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.683]
+Epoch:   1%|          | 8/1000 [00:00<00:12, 79.33it/s, loss=0.674]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.674]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.67] 
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.66]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.653]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.643]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.641]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.635]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.628]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.625]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.621]
+Epoch:   2%|         | 17/1000 [00:00<00:11, 84.50it/s, loss=0.617]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.617]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.612]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.605]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.603]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.6]  
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.595]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.591]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.589]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.585]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 86.41it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.579]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.576]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.573]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.571]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.567]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.563]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.56] 
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.557]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 87.59it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.552]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.548]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.545]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.544]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.543]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.541]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.539]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.535]
+Epoch:   4%|         | 45/1000 [00:00<00:11, 84.57it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.532]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.53] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.528]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.526]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.525]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.522]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.52] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 85.82it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.519]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.517]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.515]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.512]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.511]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.511]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.51] 
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.509]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.507]
+Epoch:   6%|         | 63/1000 [00:00<00:10, 86.89it/s, loss=0.506]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.506]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.505]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.505]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.503]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.502]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.501]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.5]  
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.5]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.498]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.497]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 90.12it/s, loss=0.496]
+Epoch:   8%|         | 83/1000 [00:00<00:10, 91.20it/s, loss=0.496]
+Epoch:   8%|         | 83/1000 [00:00<00:10, 91.20it/s, loss=0.495]
+Epoch:   8%|         | 83/1000 [00:00<00:10, 91.20it/s, loss=0.493]
+Epoch:   8%|         | 83/1000 [00:00<00:10, 91.20it/s, loss=0.492]
+Epoch:   8%|         | 83/1000 [00:00<00:10, 91.20it/s, loss=0.49] 
+Epoch:   8%|         | 83/1000 [00:01<00:10, 91.20it/s, loss=0.49]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 91.20it/s, loss=0.49]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 91.20it/s, loss=0.489]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 91.20it/s, loss=0.488]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 91.20it/s, loss=0.487]
+Epoch:   8%|         | 83/1000 [00:01<00:10, 91.20it/s, loss=0.487]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.487]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.486]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.485]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.484]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.483]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.482]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.481]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.48] 
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.41]
+Epoch:   9%|         | 93/1000 [00:01<00:10, 88.60it/s, loss=0.423]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.423]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.413]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.408]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.414]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.408]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.407]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.403]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.412]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.411]
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.41] 
+Epoch:  10%|         | 102/1000 [00:01<00:10, 88.17it/s, loss=0.407]
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.407]
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.409]
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.414]
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.417]
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.414]
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.41] 
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.412]
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.411]
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.41] 
+Epoch:  11%|         | 112/1000 [00:01<00:09, 89.75it/s, loss=0.41]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.41]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.408]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.407]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.407]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.408]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.407]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.406]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.403]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.402]
+Epoch:  12%|        | 121/1000 [00:01<00:09, 88.60it/s, loss=0.403]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.403]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.404]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.404]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.404]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.404]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.404]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.402]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.402]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.402]
+Epoch:  13%|        | 130/1000 [00:01<00:09, 88.53it/s, loss=0.402]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.402]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.402]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.401]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.4]  
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.4]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.4]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.4]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.4]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.4]
+Epoch:  14%|        | 139/1000 [00:01<00:09, 88.21it/s, loss=0.399]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.399]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.399]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.399]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.399]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.401]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.401]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.401]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.402]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.401]
+Epoch:  15%|        | 148/1000 [00:01<00:09, 87.70it/s, loss=0.399]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.399]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.399]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.399]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.398]
+Epoch:  16%|        | 157/1000 [00:01<00:09, 85.89it/s, loss=0.398]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 86.33it/s, loss=0.398]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 86.33it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 86.33it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 86.33it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 86.33it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 86.33it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 86.33it/s, loss=0.398]
+Epoch:  17%|        | 166/1000 [00:01<00:09, 86.33it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:02<00:09, 86.33it/s, loss=0.397]
+Epoch:  17%|        | 166/1000 [00:02<00:09, 86.33it/s, loss=0.397]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.397]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.398]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.398]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.398]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.399]
+Epoch:  18%|        | 175/1000 [00:02<00:09, 84.20it/s, loss=0.4]  
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.4]
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.4]  
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.4]  
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.399]
+Epoch:  18%|        | 184/1000 [00:02<00:10, 80.52it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.4]  
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.4]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.4]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.398]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.336]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 82.38it/s, loss=0.42] 
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.42]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.434]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.423]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.423]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.423]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.418]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.419]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.418]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.408]
+Epoch:  20%|        | 202/1000 [00:02<00:09, 83.95it/s, loss=0.409]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.409]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.408]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.413]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.413]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.413]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.414]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.417]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.416]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.415]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 84.82it/s, loss=0.412]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.412]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.41] 
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.407]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.409]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.407]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.404]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.403]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.402]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.402]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 82.42it/s, loss=0.401]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.39it/s, loss=0.401]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.39it/s, loss=0.401]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.39it/s, loss=0.399]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.39it/s, loss=0.4]  
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.39it/s, loss=0.399]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.39it/s, loss=0.4]  
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+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.39it/s, loss=0.399]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.39it/s, loss=0.398]
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+Epoch:  24%|       | 239/1000 [00:02<00:08, 85.99it/s, loss=0.397]
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+Epoch:  25%|       | 248/1000 [00:02<00:08, 84.97it/s, loss=0.396]
+Epoch:  25%|       | 248/1000 [00:02<00:08, 84.97it/s, loss=0.397]
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+Epoch:  25%|       | 248/1000 [00:02<00:08, 84.97it/s, loss=0.396]
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+Epoch:  25%|       | 248/1000 [00:02<00:08, 84.97it/s, loss=0.395]
+Epoch:  26%|       | 257/1000 [00:02<00:08, 86.13it/s, loss=0.395]
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+Epoch:  28%|       | 284/1000 [00:03<00:09, 79.18it/s, loss=0.393]
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+Epoch:  29%|       | 293/1000 [00:03<00:08, 78.63it/s, loss=0.394]
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+Epoch:  29%|       | 293/1000 [00:03<00:08, 78.63it/s, loss=0.394]
+Epoch:  29%|       | 293/1000 [00:03<00:08, 78.63it/s, loss=0.394]
+Epoch:  29%|       | 293/1000 [00:03<00:08, 78.63it/s, loss=0.394]
+Epoch:  29%|       | 293/1000 [00:03<00:08, 78.63it/s, loss=0.394]
+Epoch:  29%|       | 293/1000 [00:03<00:08, 78.63it/s, loss=0.371]
+Epoch:  29%|       | 293/1000 [00:03<00:08, 78.63it/s, loss=0.389]
+Epoch:  29%|       | 293/1000 [00:03<00:08, 78.63it/s, loss=0.38] 
+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.38]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.376]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.364]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.37] 
+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.379]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.378]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.383]
+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.379]
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+Epoch:  30%|       | 303/1000 [00:03<00:08, 81.70it/s, loss=0.38] 
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.38]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.381]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.379]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.383]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.381]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.379]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.379]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.381]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.385]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.387]
+Epoch:  31%|       | 312/1000 [00:03<00:08, 81.89it/s, loss=0.386]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.386]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.386]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.387]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.384]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.385]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.384]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.382]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.381]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.382]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.384]
+Epoch:  32%|      | 322/1000 [00:03<00:08, 84.16it/s, loss=0.384]
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.384]
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.382]
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.38] 
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.379]
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.38] 
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.378]
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.378]
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.379]
+Epoch:  33%|      | 332/1000 [00:03<00:07, 85.55it/s, loss=0.38] 
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+Epoch:  33%|      | 332/1000 [00:04<00:07, 85.55it/s, loss=0.38]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 87.42it/s, loss=0.38]
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+Epoch:  34%|      | 342/1000 [00:04<00:07, 87.42it/s, loss=0.383]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 87.42it/s, loss=0.384]
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+Epoch:  34%|      | 342/1000 [00:04<00:07, 87.42it/s, loss=0.383]
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+Epoch:  35%|      | 351/1000 [00:04<00:07, 87.86it/s, loss=0.384]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 87.86it/s, loss=0.384]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 87.86it/s, loss=0.384]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 87.86it/s, loss=0.383]
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+Epoch:  35%|      | 351/1000 [00:04<00:07, 87.86it/s, loss=0.383]
+Epoch:  35%|      | 351/1000 [00:04<00:07, 87.86it/s, loss=0.382]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.382]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.382]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.382]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.382]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.383]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.383]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.384]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.385]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.384]
+Epoch:  36%|      | 360/1000 [00:04<00:07, 83.16it/s, loss=0.384]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.384]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.384]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.383]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.383]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.382]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.382]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.381]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.381]
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+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.38]
+Epoch:  37%|      | 369/1000 [00:04<00:07, 84.31it/s, loss=0.38]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 86.12it/s, loss=0.38]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 86.12it/s, loss=0.379]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 86.12it/s, loss=0.379]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 86.12it/s, loss=0.379]
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+Epoch:  38%|      | 379/1000 [00:04<00:07, 86.12it/s, loss=0.379]
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+Epoch:  39%|      | 389/1000 [00:04<00:06, 88.11it/s, loss=0.38] 
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+Epoch:  49%|     | 494/1000 [00:05<00:06, 79.73it/s, loss=0.373]
+Epoch:  50%|     | 504/1000 [00:05<00:05, 83.38it/s, loss=0.373]
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+Epoch:  51%|    | 514/1000 [00:06<00:05, 87.01it/s, loss=0.382]
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+Epoch:  52%|    | 523/1000 [00:06<00:05, 86.98it/s, loss=0.369]
+Epoch:  52%|    | 523/1000 [00:06<00:05, 86.98it/s, loss=0.369]
+Epoch:  53%|    | 533/1000 [00:06<00:05, 88.17it/s, loss=0.369]
+Epoch:  53%|    | 533/1000 [00:06<00:05, 88.17it/s, loss=0.371]
+Epoch:  53%|    | 533/1000 [00:06<00:05, 88.17it/s, loss=0.372]
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+Epoch:  54%|    | 542/1000 [00:06<00:05, 87.55it/s, loss=0.368]
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+Epoch:  55%|    | 551/1000 [00:06<00:05, 83.02it/s, loss=0.368]
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+Epoch:  55%|    | 551/1000 [00:06<00:05, 83.02it/s, loss=0.37]
+Epoch:  55%|    | 551/1000 [00:06<00:05, 83.02it/s, loss=0.369]
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+Epoch:  56%|    | 561/1000 [00:06<00:05, 85.46it/s, loss=0.372]
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+Epoch:  56%|    | 561/1000 [00:06<00:05, 85.46it/s, loss=0.373]
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+Epoch:  57%|    | 570/1000 [00:06<00:05, 84.97it/s, loss=0.373]
+Epoch:  57%|    | 570/1000 [00:06<00:05, 84.97it/s, loss=0.373]
+Epoch:  57%|    | 570/1000 [00:06<00:05, 84.97it/s, loss=0.373]
+Epoch:  57%|    | 570/1000 [00:06<00:05, 84.97it/s, loss=0.373]
+Epoch:  57%|    | 570/1000 [00:06<00:05, 84.97it/s, loss=0.372]
+Epoch:  57%|    | 570/1000 [00:06<00:05, 84.97it/s, loss=0.371]
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+Epoch:  57%|    | 570/1000 [00:06<00:05, 84.97it/s, loss=0.371]
+Epoch:  57%|    | 570/1000 [00:06<00:05, 84.97it/s, loss=0.372]
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.372]
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.371]
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.372]
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.371]
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.371]
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.371]
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+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.371]
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.37] 
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.371]
+Epoch:  58%|    | 579/1000 [00:06<00:05, 83.43it/s, loss=0.371]
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+Epoch:  59%|    | 589/1000 [00:06<00:04, 85.11it/s, loss=0.37] 
+Epoch:  59%|    | 589/1000 [00:06<00:04, 85.11it/s, loss=0.37]
+Epoch:  59%|    | 589/1000 [00:06<00:04, 85.11it/s, loss=0.37]
+Epoch:  59%|    | 589/1000 [00:06<00:04, 85.11it/s, loss=0.369]
+Epoch:  59%|    | 589/1000 [00:06<00:04, 85.11it/s, loss=0.369]
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+Epoch:  60%|    | 598/1000 [00:07<00:05, 79.60it/s, loss=0.369]
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+Epoch:  60%|    | 598/1000 [00:07<00:05, 79.60it/s, loss=0.362]
+Epoch:  60%|    | 598/1000 [00:07<00:05, 79.60it/s, loss=0.395]
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+Epoch:  60%|    | 598/1000 [00:07<00:05, 79.60it/s, loss=0.368]
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+Epoch:  89%| | 889/1000 [00:10<00:01, 82.62it/s, loss=0.362]
+Epoch:  89%| | 889/1000 [00:10<00:01, 82.62it/s, loss=0.363]
+Epoch:  89%| | 889/1000 [00:10<00:01, 82.62it/s, loss=0.362]
+Epoch:  89%| | 889/1000 [00:10<00:01, 82.62it/s, loss=0.361]
+Epoch:  89%| | 889/1000 [00:10<00:01, 82.62it/s, loss=0.361]
+Epoch:  89%| | 889/1000 [00:10<00:01, 82.62it/s, loss=0.361]
+Epoch:  89%| | 889/1000 [00:10<00:01, 82.62it/s, loss=0.361]
+Epoch:  89%| | 889/1000 [00:10<00:01, 82.62it/s, loss=0.361]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.361]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.362]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.362]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.418]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.37] 
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.385]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.374]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.367]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.366]
+Epoch:  90%| | 898/1000 [00:10<00:01, 83.33it/s, loss=0.366]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.366]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.366]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.361]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.363]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.362]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.362]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.363]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.363]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.363]
+Epoch:  91%| | 907/1000 [00:10<00:01, 84.63it/s, loss=0.361]
+Epoch:  92%|| 916/1000 [00:10<00:01, 75.33it/s, loss=0.361]
+Epoch:  92%|| 916/1000 [00:11<00:01, 75.33it/s, loss=0.364]
+Epoch:  92%|| 916/1000 [00:11<00:01, 75.33it/s, loss=0.362]
+Epoch:  92%|| 916/1000 [00:11<00:01, 75.33it/s, loss=0.361]
+Epoch:  92%|| 916/1000 [00:11<00:01, 75.33it/s, loss=0.357]
+Epoch:  92%|| 916/1000 [00:11<00:01, 75.33it/s, loss=0.357]
+Epoch:  92%|| 916/1000 [00:11<00:01, 75.33it/s, loss=0.359]
+Epoch:  92%|| 916/1000 [00:11<00:01, 75.33it/s, loss=0.356]
+Epoch:  92%|| 916/1000 [00:11<00:01, 75.33it/s, loss=0.357]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.357]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.358]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.358]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.358]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.359]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.358]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.357]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.355]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.354]
+Epoch:  92%|| 924/1000 [00:11<00:01, 74.05it/s, loss=0.354]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.354]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.353]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.352]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.353]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.352]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.353]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.353]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.352]
+Epoch:  93%|| 933/1000 [00:11<00:00, 75.72it/s, loss=0.35] 
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.35]
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.349]
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.35] 
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.35]
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.349]
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.349]
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.349]
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.35] 
+Epoch:  94%|| 941/1000 [00:11<00:00, 76.54it/s, loss=0.35]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.35]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.35]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.349]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.348]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.348]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.349]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.348]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.348]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.348]
+Epoch:  95%|| 949/1000 [00:11<00:00, 71.80it/s, loss=0.349]
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.349]
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.35] 
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.351]
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.351]
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.35] 
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.35]
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.35]
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.349]
+Epoch:  96%|| 958/1000 [00:11<00:00, 74.55it/s, loss=0.35] 
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.35]
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.35] 
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.349]
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.35] 
+Epoch:  97%|| 966/1000 [00:11<00:00, 75.45it/s, loss=0.35]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.35]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.351]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.351]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.35] 
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.352]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 71.97it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:11<00:00, 75.44it/s, loss=0.351]
+Epoch:  99%|| 992/1000 [00:11<00:00, 79.39it/s, loss=0.351]
+Epoch:  99%|| 992/1000 [00:12<00:00, 79.39it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:12<00:00, 79.39it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:12<00:00, 79.39it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:12<00:00, 79.39it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:12<00:00, 79.39it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:12<00:00, 79.39it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:12<00:00, 79.39it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:12<00:00, 79.39it/s, loss=0.352]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 82.74it/s, loss=0.352]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate[test_plugin4] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.924]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.917]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.876]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.829]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.805]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.805]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.784]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.769]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.75] 
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.734]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.722]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.71] 
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.703]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.693]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 66.67it/s, loss=0.683]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.683]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.674]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.67] 
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.66]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.653]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.643]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.641]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.635]
+Epoch:   2%|         | 16/1000 [00:00<00:12, 76.84it/s, loss=0.628]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.628]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.625]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.621]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.617]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.612]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.605]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.603]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.6]  
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.595]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 76.74it/s, loss=0.591]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.591]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.589]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.585]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.581]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.579]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.576]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.573]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.571]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.567]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.38it/s, loss=0.563]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.563]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.56] 
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.557]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.555]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.552]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.548]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.545]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.544]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.543]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 82.25it/s, loss=0.541]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.541]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.539]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.535]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.533]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.533]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.532]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.53] 
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.528]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.526]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 81.53it/s, loss=0.525]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.525]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.522]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.52] 
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.519]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.519]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.517]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.515]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.512]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.511]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.03it/s, loss=0.511]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.511]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.51] 
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.509]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.507]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.506]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.505]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.505]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.503]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.502]
+Epoch:   7%|         | 69/1000 [00:00<00:11, 83.43it/s, loss=0.501]
+Epoch:   8%|         | 78/1000 [00:00<00:10, 84.39it/s, loss=0.501]
+Epoch:   8%|         | 78/1000 [00:00<00:10, 84.39it/s, loss=0.5]  
+Epoch:   8%|         | 78/1000 [00:00<00:10, 84.39it/s, loss=0.5]
+Epoch:   8%|         | 78/1000 [00:00<00:10, 84.39it/s, loss=0.498]
+Epoch:   8%|         | 78/1000 [00:00<00:10, 84.39it/s, loss=0.497]
+Epoch:   8%|         | 78/1000 [00:01<00:10, 84.39it/s, loss=0.496]
+Epoch:   8%|         | 78/1000 [00:01<00:10, 84.39it/s, loss=0.495]
+Epoch:   8%|         | 78/1000 [00:01<00:10, 84.39it/s, loss=0.493]
+Epoch:   8%|         | 78/1000 [00:01<00:10, 84.39it/s, loss=0.492]
+Epoch:   8%|         | 78/1000 [00:01<00:10, 84.39it/s, loss=0.49] 
+Epoch:   8%|         | 78/1000 [00:01<00:10, 84.39it/s, loss=0.49]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.49]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.49]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.489]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.488]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.487]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.487]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.486]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.485]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.484]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 86.44it/s, loss=0.483]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.483]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.482]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.481]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.48] 
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.41]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.423]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.413]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.408]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.414]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.408]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 86.93it/s, loss=0.407]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.407]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.403]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.412]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.411]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.41] 
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.407]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.409]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.414]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.417]
+Epoch:  11%|         | 107/1000 [00:01<00:09, 89.60it/s, loss=0.414]
+Epoch:  12%|        | 116/1000 [00:01<00:09, 88.62it/s, loss=0.414]
+Epoch:  12%|        | 116/1000 [00:01<00:09, 88.62it/s, loss=0.41] 
+Epoch:  12%|        | 116/1000 [00:01<00:09, 88.62it/s, loss=0.412]
+Epoch:  12%|        | 116/1000 [00:01<00:09, 88.62it/s, loss=0.411]
+Epoch:  12%|        | 116/1000 [00:01<00:09, 88.62it/s, loss=0.41] 
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+Epoch:  12%|        | 116/1000 [00:01<00:09, 88.62it/s, loss=0.408]
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+Epoch:  12%|        | 125/1000 [00:01<00:09, 88.27it/s, loss=0.406]
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+Epoch:  12%|        | 125/1000 [00:01<00:09, 88.27it/s, loss=0.404]
+Epoch:  12%|        | 125/1000 [00:01<00:09, 88.27it/s, loss=0.404]
+Epoch:  12%|        | 125/1000 [00:01<00:09, 88.27it/s, loss=0.404]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.404]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.404]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.402]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.402]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.402]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.402]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.402]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.401]
+Epoch:  13%|        | 134/1000 [00:01<00:09, 87.02it/s, loss=0.4]  
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+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.4]
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+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.4]
+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.4]
+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.399]
+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.399]
+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.399]
+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.399]
+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.401]
+Epoch:  14%|        | 143/1000 [00:01<00:09, 87.34it/s, loss=0.401]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.401]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.401]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.402]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.401]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.399]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.399]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.399]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.398]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.398]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.398]
+Epoch:  15%|        | 153/1000 [00:01<00:09, 89.56it/s, loss=0.398]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.398]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.398]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.398]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.398]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.397]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.397]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.397]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.397]
+Epoch:  16%|        | 163/1000 [00:01<00:09, 89.34it/s, loss=0.397]
+Epoch:  16%|        | 163/1000 [00:02<00:09, 89.34it/s, loss=0.398]
+Epoch:  16%|        | 163/1000 [00:02<00:09, 89.34it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.397]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.398]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.399]
+Epoch:  17%|        | 173/1000 [00:02<00:09, 89.89it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.4]  
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.4]  
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.4]  
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 90.76it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.4]  
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.4]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.4]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.399]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.398]
+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.336]
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+Epoch:  19%|        | 193/1000 [00:02<00:09, 89.39it/s, loss=0.434]
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+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.31it/s, loss=0.408]
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+Epoch:  21%|        | 212/1000 [00:02<00:09, 84.31it/s, loss=0.41] 
+Epoch:  22%|       | 221/1000 [00:02<00:09, 83.74it/s, loss=0.41]
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+Epoch:  22%|       | 221/1000 [00:02<00:09, 83.74it/s, loss=0.407]
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+Epoch:  22%|       | 221/1000 [00:02<00:09, 83.74it/s, loss=0.403]
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+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.393]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.393]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.393]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.393]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.393]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.393]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.394]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.394]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.394]
+Epoch:  29%|       | 286/1000 [00:03<00:08, 87.96it/s, loss=0.394]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.394]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.394]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.394]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.394]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.394]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.394]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.371]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.389]
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.38] 
+Epoch:  30%|       | 295/1000 [00:03<00:08, 87.23it/s, loss=0.376]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.376]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.364]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.37] 
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.379]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.378]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.383]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.379]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.386]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.38] 
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.381]
+Epoch:  30%|       | 304/1000 [00:03<00:08, 83.76it/s, loss=0.379]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.379]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.383]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.381]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.379]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.379]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.381]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.385]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.387]
+Epoch:  31%|      | 314/1000 [00:03<00:07, 86.25it/s, loss=0.386]
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+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.387]
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+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.385]
+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.384]
+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.382]
+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.381]
+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.382]
+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.384]
+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.384]
+Epoch:  32%|      | 324/1000 [00:03<00:07, 87.74it/s, loss=0.382]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.382]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.38] 
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.379]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.38] 
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.378]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.378]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.379]
+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.38] 
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+Epoch:  33%|      | 333/1000 [00:03<00:07, 85.32it/s, loss=0.38]
+Epoch:  34%|      | 342/1000 [00:03<00:07, 85.40it/s, loss=0.38]
+Epoch:  34%|      | 342/1000 [00:03<00:07, 85.40it/s, loss=0.381]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.382]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.381]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.382]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.383]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.384]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.384]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.385]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.383]
+Epoch:  34%|      | 342/1000 [00:04<00:07, 85.40it/s, loss=0.385]
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+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.385]
+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.384]
+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.384]
+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.384]
+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.383]
+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.382]
+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.383]
+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.382]
+Epoch:  35%|      | 352/1000 [00:04<00:07, 86.33it/s, loss=0.382]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 86.53it/s, loss=0.382]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 86.53it/s, loss=0.382]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 86.53it/s, loss=0.382]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 86.53it/s, loss=0.383]
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+Epoch:  36%|      | 361/1000 [00:04<00:07, 86.53it/s, loss=0.384]
+Epoch:  36%|      | 361/1000 [00:04<00:07, 86.53it/s, loss=0.384]
+Epoch:  37%|      | 370/1000 [00:04<00:07, 85.86it/s, loss=0.384]
+Epoch:  37%|      | 370/1000 [00:04<00:07, 85.86it/s, loss=0.383]
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+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.38]
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+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.379]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.379]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.38] 
+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.38]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.379]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.38] 
+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.38]
+Epoch:  38%|      | 379/1000 [00:04<00:07, 83.34it/s, loss=0.38]
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+Epoch:  39%|      | 389/1000 [00:04<00:07, 85.59it/s, loss=0.379]
+Epoch:  39%|      | 389/1000 [00:04<00:07, 85.59it/s, loss=0.38] 
+Epoch:  39%|      | 389/1000 [00:04<00:07, 85.59it/s, loss=0.379]
+Epoch:  39%|      | 389/1000 [00:04<00:07, 85.59it/s, loss=0.38] 
+Epoch:  39%|      | 389/1000 [00:04<00:07, 85.59it/s, loss=0.381]
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+Epoch:  49%|     | 490/1000 [00:05<00:05, 85.71it/s, loss=0.385]
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+Epoch:  53%|    | 527/1000 [00:06<00:05, 88.30it/s, loss=0.372]
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+Epoch:  55%|    | 545/1000 [00:06<00:05, 84.95it/s, loss=0.369]
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+Epoch:  57%|    | 573/1000 [00:06<00:04, 88.67it/s, loss=0.371]
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+Epoch:  57%|    | 573/1000 [00:06<00:04, 88.67it/s, loss=0.371]
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+Epoch:  59%|    | 591/1000 [00:06<00:04, 86.88it/s, loss=0.37]
+Epoch:  59%|    | 591/1000 [00:06<00:04, 86.88it/s, loss=0.369]
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+Epoch:  87%| | 870/1000 [00:10<00:01, 83.26it/s, loss=0.363]
+Epoch:  87%| | 870/1000 [00:10<00:01, 83.26it/s, loss=0.363]
+Epoch:  87%| | 870/1000 [00:10<00:01, 83.26it/s, loss=0.363]
+Epoch:  87%| | 870/1000 [00:10<00:01, 83.26it/s, loss=0.363]
+Epoch:  87%| | 870/1000 [00:10<00:01, 83.26it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  88%| | 879/1000 [00:10<00:01, 82.74it/s, loss=0.362]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.362]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.362]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.362]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.362]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.363]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.362]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.361]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.361]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.361]
+Epoch:  89%| | 888/1000 [00:10<00:01, 82.00it/s, loss=0.361]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.361]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.361]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.362]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.362]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.418]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.37] 
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.385]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.374]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.367]
+Epoch:  90%| | 897/1000 [00:10<00:01, 78.47it/s, loss=0.366]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.366]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.366]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.366]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.361]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.363]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.362]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.362]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.363]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.363]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.363]
+Epoch:  91%| | 906/1000 [00:10<00:01, 79.35it/s, loss=0.361]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.361]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.364]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.362]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.361]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.357]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.357]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.359]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.356]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.357]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.358]
+Epoch:  92%|| 916/1000 [00:10<00:01, 83.02it/s, loss=0.358]
+Epoch:  93%|| 926/1000 [00:10<00:00, 84.50it/s, loss=0.358]
+Epoch:  93%|| 926/1000 [00:10<00:00, 84.50it/s, loss=0.358]
+Epoch:  93%|| 926/1000 [00:11<00:00, 84.50it/s, loss=0.359]
+Epoch:  93%|| 926/1000 [00:11<00:00, 84.50it/s, loss=0.358]
+Epoch:  93%|| 926/1000 [00:11<00:00, 84.50it/s, loss=0.357]
+Epoch:  93%|| 926/1000 [00:11<00:00, 84.50it/s, loss=0.355]
+Epoch:  93%|| 926/1000 [00:11<00:00, 84.50it/s, loss=0.354]
+Epoch:  93%|| 926/1000 [00:11<00:00, 84.50it/s, loss=0.354]
+Epoch:  93%|| 926/1000 [00:11<00:00, 84.50it/s, loss=0.353]
+Epoch:  93%|| 926/1000 [00:11<00:00, 84.50it/s, loss=0.352]
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.352]
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.353]
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.352]
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.353]
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.353]
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.352]
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.35] 
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.349]
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.35] 
+Epoch:  94%|| 935/1000 [00:11<00:00, 85.29it/s, loss=0.35]
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.35]
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.349]
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.349]
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.349]
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.35] 
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.35]
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.35]
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.349]
+Epoch:  94%|| 944/1000 [00:11<00:00, 78.77it/s, loss=0.348]
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.348]
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.348]
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.349]
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.348]
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.348]
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.348]
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.349]
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.35] 
+Epoch:  95%|| 952/1000 [00:11<00:00, 78.13it/s, loss=0.351]
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.351]
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.351]
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.35] 
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.35]
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.35]
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.349]
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.35] 
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.349]
+Epoch:  96%|| 960/1000 [00:11<00:00, 76.45it/s, loss=0.35] 
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.35]
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.349]
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.349]
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.349]
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.349]
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.35] 
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.35]
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.351]
+Epoch:  97%|| 968/1000 [00:11<00:00, 76.74it/s, loss=0.351]
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.351]
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.35] 
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.352]
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.353]
+Epoch:  98%|| 976/1000 [00:11<00:00, 77.24it/s, loss=0.353]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.353]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.353]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.353]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.352]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.353]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.352]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.352]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.352]
+Epoch:  98%|| 984/1000 [00:11<00:00, 76.35it/s, loss=0.351]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.351]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 74.78it/s, loss=0.352]
+Epoch: 100%|| 1000/1000 [00:11<00:00, 75.33it/s, loss=0.352]
+Epoch: 100%|| 1000/1000 [00:11<00:00, 83.55it/s, loss=0.352]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate[test_plugin5] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.924]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.917]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.876]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.829]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.805]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.784]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.784]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.769]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.75] 
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.734]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.722]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.71] 
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.703]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.693]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.683]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.674]
+Epoch:   1%|          | 8/1000 [00:00<00:13, 72.65it/s, loss=0.67] 
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.67]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.66]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.653]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.643]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.641]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.635]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.628]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.625]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.621]
+Epoch:   2%|         | 18/1000 [00:00<00:11, 84.53it/s, loss=0.617]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.617]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.612]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.605]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.603]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.6]  
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.595]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.591]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.589]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.585]
+Epoch:   3%|         | 27/1000 [00:00<00:11, 85.22it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.581]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.579]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.576]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.573]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.571]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.567]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.563]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.56] 
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.557]
+Epoch:   4%|         | 36/1000 [00:00<00:11, 84.78it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.555]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.552]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.548]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.545]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.544]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.543]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.541]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.539]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.535]
+Epoch:   4%|         | 45/1000 [00:00<00:12, 77.91it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.533]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.532]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.53] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.528]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.526]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.525]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.522]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.52] 
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.519]
+Epoch:   5%|         | 54/1000 [00:00<00:11, 79.98it/s, loss=0.519]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.519]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.517]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.515]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.512]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.511]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.511]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.51] 
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.509]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.507]
+Epoch:   6%|         | 64/1000 [00:00<00:11, 83.45it/s, loss=0.506]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.506]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.505]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.505]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.503]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.502]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.501]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.5]  
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.5]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.498]
+Epoch:   7%|         | 73/1000 [00:00<00:10, 84.47it/s, loss=0.497]
+Epoch:   8%|         | 82/1000 [00:00<00:10, 84.35it/s, loss=0.497]
+Epoch:   8%|         | 82/1000 [00:00<00:10, 84.35it/s, loss=0.496]
+Epoch:   8%|         | 82/1000 [00:01<00:10, 84.35it/s, loss=0.495]
+Epoch:   8%|         | 82/1000 [00:01<00:10, 84.35it/s, loss=0.493]
+Epoch:   8%|         | 82/1000 [00:01<00:10, 84.35it/s, loss=0.492]
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+Epoch:   8%|         | 82/1000 [00:01<00:10, 84.35it/s, loss=0.49]
+Epoch:   8%|         | 82/1000 [00:01<00:10, 84.35it/s, loss=0.489]
+Epoch:   8%|         | 82/1000 [00:01<00:10, 84.35it/s, loss=0.488]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.488]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.487]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.487]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.486]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.485]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.484]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.483]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.482]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.481]
+Epoch:   9%|         | 91/1000 [00:01<00:11, 82.39it/s, loss=0.48] 
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.48]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.41]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.423]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.413]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.408]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.414]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.408]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.407]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.403]
+Epoch:  10%|         | 100/1000 [00:01<00:10, 84.52it/s, loss=0.412]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.412]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.411]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.41] 
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.407]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.409]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.414]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.417]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.414]
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.41] 
+Epoch:  11%|         | 109/1000 [00:01<00:10, 85.75it/s, loss=0.412]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.412]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.411]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.41] 
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.41]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.408]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.407]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.407]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.408]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.407]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.406]
+Epoch:  12%|        | 118/1000 [00:01<00:10, 81.01it/s, loss=0.403]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.403]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.402]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.403]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.404]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.402]
+Epoch:  13%|        | 128/1000 [00:01<00:10, 84.18it/s, loss=0.402]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.402]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.402]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.402]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.402]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.401]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.4]  
+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.4]
+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.4]
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+Epoch:  14%|        | 137/1000 [00:01<00:10, 83.52it/s, loss=0.4]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.4]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.399]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.399]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.399]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.399]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.401]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.401]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.401]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.402]
+Epoch:  15%|        | 147/1000 [00:01<00:09, 86.37it/s, loss=0.401]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.401]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.399]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.399]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.399]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.398]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.398]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.398]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.398]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.398]
+Epoch:  16%|        | 156/1000 [00:01<00:10, 83.40it/s, loss=0.398]
+Epoch:  16%|        | 165/1000 [00:01<00:09, 84.22it/s, loss=0.398]
+Epoch:  16%|        | 165/1000 [00:01<00:09, 84.22it/s, loss=0.398]
+Epoch:  16%|        | 165/1000 [00:01<00:09, 84.22it/s, loss=0.397]
+Epoch:  16%|        | 165/1000 [00:02<00:09, 84.22it/s, loss=0.397]
+Epoch:  16%|        | 165/1000 [00:02<00:09, 84.22it/s, loss=0.397]
+Epoch:  16%|        | 165/1000 [00:02<00:09, 84.22it/s, loss=0.397]
+Epoch:  16%|        | 165/1000 [00:02<00:09, 84.22it/s, loss=0.397]
+Epoch:  16%|        | 165/1000 [00:02<00:09, 84.22it/s, loss=0.398]
+Epoch:  16%|        | 165/1000 [00:02<00:09, 84.22it/s, loss=0.397]
+Epoch:  16%|        | 165/1000 [00:02<00:09, 84.22it/s, loss=0.397]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.397]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.397]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.398]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.398]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.398]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.399]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.399]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.399]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.399]
+Epoch:  17%|        | 174/1000 [00:02<00:09, 85.77it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.4]  
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.4]  
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.399]
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.4]  
+Epoch:  18%|        | 183/1000 [00:02<00:09, 86.48it/s, loss=0.399]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.399]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.399]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.399]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.399]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.4]  
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.4]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.4]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.399]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.398]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.336]
+Epoch:  19%|        | 192/1000 [00:02<00:09, 82.27it/s, loss=0.42] 
+Epoch:  20%|        | 202/1000 [00:02<00:09, 85.14it/s, loss=0.42]
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+Epoch:  20%|        | 202/1000 [00:02<00:09, 85.14it/s, loss=0.418]
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+Epoch:  20%|        | 202/1000 [00:02<00:09, 85.14it/s, loss=0.408]
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+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.409]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.408]
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+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.413]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.413]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.414]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.417]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.416]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.415]
+Epoch:  21%|        | 211/1000 [00:02<00:09, 85.54it/s, loss=0.412]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.44it/s, loss=0.412]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.44it/s, loss=0.41] 
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.44it/s, loss=0.407]
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+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.44it/s, loss=0.407]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.44it/s, loss=0.404]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.44it/s, loss=0.403]
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+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.44it/s, loss=0.402]
+Epoch:  22%|       | 220/1000 [00:02<00:09, 86.44it/s, loss=0.401]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.401]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.401]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.399]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.4]  
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.399]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.4]  
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+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.399]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.399]
+Epoch:  23%|       | 229/1000 [00:02<00:09, 84.51it/s, loss=0.398]
+Epoch:  24%|       | 238/1000 [00:02<00:09, 77.35it/s, loss=0.398]
+Epoch:  24%|       | 238/1000 [00:02<00:09, 77.35it/s, loss=0.397]
+Epoch:  24%|       | 238/1000 [00:02<00:09, 77.35it/s, loss=0.397]
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+Epoch:  24%|       | 238/1000 [00:02<00:09, 77.35it/s, loss=0.397]
+Epoch:  24%|       | 238/1000 [00:02<00:09, 77.35it/s, loss=0.396]
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+Epoch:  24%|       | 238/1000 [00:02<00:09, 77.35it/s, loss=0.396]
+Epoch:  24%|       | 238/1000 [00:02<00:09, 77.35it/s, loss=0.397]
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+Epoch:  25%|       | 247/1000 [00:02<00:09, 80.42it/s, loss=0.396]
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+Epoch:  25%|       | 247/1000 [00:03<00:09, 80.42it/s, loss=0.396]
+Epoch:  25%|       | 247/1000 [00:03<00:09, 80.42it/s, loss=0.397]
+Epoch:  25%|       | 247/1000 [00:03<00:09, 80.42it/s, loss=0.397]
+Epoch:  25%|       | 247/1000 [00:03<00:09, 80.42it/s, loss=0.396]
+Epoch:  25%|       | 247/1000 [00:03<00:09, 80.42it/s, loss=0.396]
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+Epoch:  25%|       | 247/1000 [00:03<00:09, 80.42it/s, loss=0.395]
+Epoch:  26%|       | 257/1000 [00:03<00:08, 82.93it/s, loss=0.395]
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+Epoch:  26%|       | 257/1000 [00:03<00:08, 82.93it/s, loss=0.394]
+Epoch:  26%|       | 257/1000 [00:03<00:08, 82.93it/s, loss=0.394]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.30it/s, loss=0.394]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.30it/s, loss=0.394]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.30it/s, loss=0.394]
+Epoch:  27%|       | 266/1000 [00:03<00:08, 82.30it/s, loss=0.394]
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+Epoch:  28%|       | 275/1000 [00:03<00:08, 83.65it/s, loss=0.395]
+Epoch:  28%|       | 275/1000 [00:03<00:08, 83.65it/s, loss=0.395]
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+Epoch:  28%|       | 275/1000 [00:03<00:08, 83.65it/s, loss=0.393]
+Epoch:  28%|       | 275/1000 [00:03<00:08, 83.65it/s, loss=0.393]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 85.50it/s, loss=0.393]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 85.50it/s, loss=0.393]
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+Epoch:  28%|       | 285/1000 [00:03<00:08, 85.50it/s, loss=0.393]
+Epoch:  28%|       | 285/1000 [00:03<00:08, 85.50it/s, loss=0.393]
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+Epoch:  29%|       | 294/1000 [00:03<00:08, 86.36it/s, loss=0.394]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 86.36it/s, loss=0.394]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 86.36it/s, loss=0.394]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 86.36it/s, loss=0.394]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 86.36it/s, loss=0.371]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 86.36it/s, loss=0.389]
+Epoch:  29%|       | 294/1000 [00:03<00:08, 86.36it/s, loss=0.38] 
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+Epoch:  30%|       | 304/1000 [00:03<00:07, 87.88it/s, loss=0.379]
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+Epoch:  31%|      | 314/1000 [00:03<00:07, 88.68it/s, loss=0.379]
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+Epoch:  32%|      | 323/1000 [00:03<00:07, 86.47it/s, loss=0.381]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 86.47it/s, loss=0.382]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 86.47it/s, loss=0.384]
+Epoch:  32%|      | 323/1000 [00:03<00:07, 86.47it/s, loss=0.384]
+Epoch:  33%|      | 332/1000 [00:03<00:08, 83.46it/s, loss=0.384]
+Epoch:  33%|      | 332/1000 [00:03<00:08, 83.46it/s, loss=0.382]
+Epoch:  33%|      | 332/1000 [00:03<00:08, 83.46it/s, loss=0.38] 
+Epoch:  33%|      | 332/1000 [00:03<00:08, 83.46it/s, loss=0.379]
+Epoch:  33%|      | 332/1000 [00:04<00:08, 83.46it/s, loss=0.38] 
+Epoch:  33%|      | 332/1000 [00:04<00:08, 83.46it/s, loss=0.378]
+Epoch:  33%|      | 332/1000 [00:04<00:08, 83.46it/s, loss=0.378]
+Epoch:  33%|      | 332/1000 [00:04<00:08, 83.46it/s, loss=0.379]
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+Epoch:  53%|    | 529/1000 [00:06<00:05, 89.84it/s, loss=0.372]
+Epoch:  53%|    | 529/1000 [00:06<00:05, 89.84it/s, loss=0.372]
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+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.364]
+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.365]
+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.364]
+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.364]
+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.364]
+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.364]
+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.364]
+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.364]
+Epoch:  86%| | 856/1000 [00:10<00:01, 84.41it/s, loss=0.364]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.364]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.364]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.364]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.363]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.363]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.364]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.364]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.364]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.363]
+Epoch:  86%| | 865/1000 [00:10<00:01, 84.01it/s, loss=0.364]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.364]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.363]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.363]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.363]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.363]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.362]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.362]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.362]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.362]
+Epoch:  87%| | 874/1000 [00:10<00:01, 80.85it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.362]
+Epoch:  88%| | 883/1000 [00:10<00:01, 82.18it/s, loss=0.363]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.363]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.362]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.361]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.361]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.361]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.361]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.361]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.362]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.362]
+Epoch:  89%| | 892/1000 [00:10<00:01, 84.21it/s, loss=0.418]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.418]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.37] 
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.385]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.374]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.367]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.366]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.366]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.366]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.361]
+Epoch:  90%| | 901/1000 [00:10<00:01, 80.69it/s, loss=0.363]
+Epoch:  91%| | 910/1000 [00:10<00:01, 78.93it/s, loss=0.363]
+Epoch:  91%| | 910/1000 [00:10<00:01, 78.93it/s, loss=0.362]
+Epoch:  91%| | 910/1000 [00:10<00:01, 78.93it/s, loss=0.362]
+Epoch:  91%| | 910/1000 [00:10<00:01, 78.93it/s, loss=0.363]
+Epoch:  91%| | 910/1000 [00:10<00:01, 78.93it/s, loss=0.363]
+Epoch:  91%| | 910/1000 [00:10<00:01, 78.93it/s, loss=0.363]
+Epoch:  91%| | 910/1000 [00:10<00:01, 78.93it/s, loss=0.361]
+Epoch:  91%| | 910/1000 [00:10<00:01, 78.93it/s, loss=0.364]
+Epoch:  91%| | 910/1000 [00:11<00:01, 78.93it/s, loss=0.362]
+Epoch:  91%| | 910/1000 [00:11<00:01, 78.93it/s, loss=0.361]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.361]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.357]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.357]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.359]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.356]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.357]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.358]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.358]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.358]
+Epoch:  92%|| 919/1000 [00:11<00:01, 80.81it/s, loss=0.359]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.359]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.358]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.357]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.355]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.354]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.354]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.353]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.352]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.353]
+Epoch:  93%|| 928/1000 [00:11<00:00, 81.20it/s, loss=0.352]
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.352]
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.353]
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.353]
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.352]
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.35] 
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.349]
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.35] 
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.35]
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.349]
+Epoch:  94%|| 937/1000 [00:11<00:00, 81.11it/s, loss=0.349]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.349]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.349]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.35] 
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.35]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.35]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.349]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.348]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.348]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.349]
+Epoch:  95%|| 946/1000 [00:11<00:00, 83.28it/s, loss=0.348]
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.348]
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.348]
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.348]
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.349]
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.35] 
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.351]
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.351]
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.35] 
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.35]
+Epoch:  96%|| 955/1000 [00:11<00:00, 80.47it/s, loss=0.35]
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.35]
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.349]
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.35] 
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.349]
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.35] 
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.349]
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.349]
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.349]
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.349]
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.35] 
+Epoch:  96%|| 964/1000 [00:11<00:00, 81.60it/s, loss=0.35]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.35]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.351]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.351]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.35] 
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.352]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.353]
+Epoch:  97%|| 974/1000 [00:11<00:00, 84.56it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.353]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.352]
+Epoch:  98%|| 983/1000 [00:11<00:00, 82.30it/s, loss=0.351]
+Epoch:  99%|| 992/1000 [00:11<00:00, 83.28it/s, loss=0.351]
+Epoch:  99%|| 992/1000 [00:11<00:00, 83.28it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 83.28it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 83.28it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 83.28it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 83.28it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 83.28it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:11<00:00, 83.28it/s, loss=0.352]
+Epoch:  99%|| 992/1000 [00:12<00:00, 83.28it/s, loss=0.352]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 83.32it/s, loss=0.352]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin0] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.936]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.931]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.896]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.849]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.849]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.813]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.78] 
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.763]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.74] 
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.724]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.707]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.691]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.679]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.669]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.24it/s, loss=0.656]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.656]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.647]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.642]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.632]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.624]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.613]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.61] 
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.604]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.596]
+Epoch:   2%|         | 16/1000 [00:00<00:13, 74.37it/s, loss=0.592]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.592]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.587]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.582]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.577]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.569]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.566]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.562]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.557]
+Epoch:   2%|         | 25/1000 [00:00<00:12, 78.21it/s, loss=0.552]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.552]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.549]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.544]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.54] 
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.536]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.533]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.529]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.527]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 77.98it/s, loss=0.523]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.523]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.52] 
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.516]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.513]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.51] 
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.507]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.504]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.501]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.499]
+Epoch:   4%|         | 41/1000 [00:00<00:12, 76.61it/s, loss=0.498]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 80.53it/s, loss=0.498]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 80.53it/s, loss=0.495]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 80.53it/s, loss=0.493]
+Epoch:   5%|         | 50/1000 [00:00<00:11, 80.53it/s, loss=0.49] 
+Epoch:   5%|         | 50/1000 [00:00<00:11, 80.53it/s, loss=0.487]
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+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.46it/s, loss=0.315]
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+Epoch:  28%|       | 278/1000 [00:03<00:09, 77.41it/s, loss=0.314]
+Epoch:  28%|       | 278/1000 [00:03<00:09, 77.41it/s, loss=0.314]
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+Epoch:  29%|       | 287/1000 [00:03<00:08, 79.79it/s, loss=0.313]
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+Epoch:  29%|       | 287/1000 [00:03<00:08, 79.79it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:08, 79.79it/s, loss=0.314]
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+Epoch:  30%|       | 296/1000 [00:03<00:08, 80.56it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 80.56it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 80.56it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 80.56it/s, loss=0.314]
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+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.56it/s, loss=0.295]
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+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.56it/s, loss=0.312]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 82.56it/s, loss=0.311]
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+Epoch:  31%|      | 314/1000 [00:03<00:08, 81.84it/s, loss=0.308]
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+Epoch:  45%|     | 447/1000 [00:05<00:07, 78.55it/s, loss=0.316]
+Epoch:  45%|     | 447/1000 [00:05<00:07, 78.55it/s, loss=0.315]
+Epoch:  45%|     | 447/1000 [00:05<00:07, 78.55it/s, loss=0.315]
+Epoch:  45%|     | 447/1000 [00:05<00:07, 78.55it/s, loss=0.316]
+Epoch:  45%|     | 447/1000 [00:05<00:07, 78.55it/s, loss=0.315]
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+Epoch:  45%|     | 447/1000 [00:05<00:07, 78.55it/s, loss=0.314]
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+Epoch:  46%|     | 455/1000 [00:05<00:07, 76.77it/s, loss=0.314]
+Epoch:  46%|     | 455/1000 [00:05<00:07, 76.77it/s, loss=0.314]
+Epoch:  46%|     | 455/1000 [00:05<00:07, 76.77it/s, loss=0.314]
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+Epoch:  46%|     | 455/1000 [00:05<00:07, 76.77it/s, loss=0.311]
+Epoch:  46%|     | 455/1000 [00:05<00:07, 76.77it/s, loss=0.31] 
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+Epoch:  46%|     | 465/1000 [00:05<00:06, 81.21it/s, loss=0.31]
+Epoch:  46%|     | 465/1000 [00:05<00:06, 81.21it/s, loss=0.31]
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+Epoch:  46%|     | 465/1000 [00:05<00:06, 81.21it/s, loss=0.31]
+Epoch:  46%|     | 465/1000 [00:05<00:06, 81.21it/s, loss=0.31]
+Epoch:  46%|     | 465/1000 [00:05<00:06, 81.21it/s, loss=0.311]
+Epoch:  46%|     | 465/1000 [00:05<00:06, 81.21it/s, loss=0.31] 
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+Epoch:  46%|     | 465/1000 [00:05<00:06, 81.21it/s, loss=0.31] 
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+Epoch:  49%|     | 492/1000 [00:06<00:06, 81.77it/s, loss=0.309]
+Epoch:  49%|     | 492/1000 [00:06<00:06, 81.77it/s, loss=0.309]
+Epoch:  49%|     | 492/1000 [00:06<00:06, 81.77it/s, loss=0.309]
+Epoch:  49%|     | 492/1000 [00:06<00:06, 81.77it/s, loss=0.309]
+Epoch:  49%|     | 492/1000 [00:06<00:06, 81.77it/s, loss=0.309]
+Epoch:  49%|     | 492/1000 [00:06<00:06, 81.77it/s, loss=0.308]
+Epoch:  49%|     | 492/1000 [00:06<00:06, 81.77it/s, loss=0.308]
+Epoch:  49%|     | 492/1000 [00:06<00:06, 81.77it/s, loss=0.322]
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+Epoch:  50%|     | 501/1000 [00:06<00:05, 83.67it/s, loss=0.324]
+Epoch:  50%|     | 501/1000 [00:06<00:05, 83.67it/s, loss=0.294]
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+Epoch:  50%|     | 501/1000 [00:06<00:05, 83.67it/s, loss=0.295]
+Epoch:  50%|     | 501/1000 [00:06<00:05, 83.67it/s, loss=0.299]
+Epoch:  50%|     | 501/1000 [00:06<00:05, 83.67it/s, loss=0.308]
+Epoch:  50%|     | 501/1000 [00:06<00:05, 83.67it/s, loss=0.311]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.311]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.308]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.305]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.308]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.309]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.308]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.308]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.304]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.301]
+Epoch:  51%|     | 510/1000 [00:06<00:05, 83.78it/s, loss=0.3]  
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+Epoch:  52%|    | 519/1000 [00:06<00:05, 83.72it/s, loss=0.302]
+Epoch:  52%|    | 519/1000 [00:06<00:05, 83.72it/s, loss=0.302]
+Epoch:  52%|    | 519/1000 [00:06<00:05, 83.72it/s, loss=0.303]
+Epoch:  52%|    | 519/1000 [00:06<00:05, 83.72it/s, loss=0.303]
+Epoch:  52%|    | 519/1000 [00:06<00:05, 83.72it/s, loss=0.301]
+Epoch:  52%|    | 519/1000 [00:06<00:05, 83.72it/s, loss=0.3]  
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+Epoch:  83%| | 829/1000 [00:10<00:02, 76.22it/s, loss=0.292]
+Epoch:  83%| | 829/1000 [00:10<00:02, 76.22it/s, loss=0.292]
+Epoch:  83%| | 829/1000 [00:10<00:02, 76.22it/s, loss=0.291]
+Epoch:  83%| | 829/1000 [00:10<00:02, 76.22it/s, loss=0.291]
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.291]
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.291]
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.291]
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.29] 
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.291]
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.291]
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.29] 
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.291]
+Epoch:  84%| | 838/1000 [00:10<00:02, 78.12it/s, loss=0.291]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.291]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.293]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.293]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.293]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.293]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.293]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.294]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.293]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.292]
+Epoch:  85%| | 846/1000 [00:10<00:02, 75.99it/s, loss=0.294]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.294]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.294]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.293]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.294]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.294]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.293]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.293]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.293]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.292]
+Epoch:  86%| | 855/1000 [00:10<00:01, 78.03it/s, loss=0.292]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.292]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.292]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.292]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.291]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.291]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.291]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.29] 
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.291]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.291]
+Epoch:  86%| | 864/1000 [00:10<00:01, 80.26it/s, loss=0.29] 
+Epoch:  87%| | 873/1000 [00:10<00:01, 81.91it/s, loss=0.29]
+Epoch:  87%| | 873/1000 [00:10<00:01, 81.91it/s, loss=0.291]
+Epoch:  87%| | 873/1000 [00:10<00:01, 81.91it/s, loss=0.29] 
+Epoch:  87%| | 873/1000 [00:10<00:01, 81.91it/s, loss=0.29]
+Epoch:  87%| | 873/1000 [00:10<00:01, 81.91it/s, loss=0.29]
+Epoch:  87%| | 873/1000 [00:10<00:01, 81.91it/s, loss=0.29]
+Epoch:  87%| | 873/1000 [00:10<00:01, 81.91it/s, loss=0.289]
+Epoch:  87%| | 873/1000 [00:11<00:01, 81.91it/s, loss=0.289]
+Epoch:  87%| | 873/1000 [00:11<00:01, 81.91it/s, loss=0.289]
+Epoch:  87%| | 873/1000 [00:11<00:01, 81.91it/s, loss=0.289]
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.289]
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.29] 
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.289]
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.289]
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.289]
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.289]
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.29] 
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.29]
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.29]
+Epoch:  88%| | 882/1000 [00:11<00:01, 80.23it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.289]
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.289]
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.29] 
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.289]
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.29] 
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 81.58it/s, loss=0.29]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.29]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.343]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.3]  
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.3]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.284]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.284]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.286]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.286]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.289]
+Epoch:  90%| | 900/1000 [00:11<00:01, 81.14it/s, loss=0.284]
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.284]
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.287]
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.288]
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.289]
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.29] 
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.291]
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.29] 
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.29]
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.293]
+Epoch:  91%| | 909/1000 [00:11<00:01, 81.90it/s, loss=0.289]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.289]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.289]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.286]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.286]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.289]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.286]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.287]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.287]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.287]
+Epoch:  92%|| 918/1000 [00:11<00:00, 83.84it/s, loss=0.287]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.287]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.288]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.287]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.286]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.284]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.282]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.282]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.281]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.281]
+Epoch:  93%|| 927/1000 [00:11<00:00, 83.78it/s, loss=0.281]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.281]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.281]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.281]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.28] 
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.28]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.278]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.277]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.278]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.278]
+Epoch:  94%|| 936/1000 [00:11<00:00, 81.12it/s, loss=0.278]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.278]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.278]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.278]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.278]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.278]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.278]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.277]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.276]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.277]
+Epoch:  94%|| 945/1000 [00:11<00:00, 82.06it/s, loss=0.277]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.277]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.277]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.277]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.277]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.277]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.278]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.279]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.279]
+Epoch:  95%|| 954/1000 [00:11<00:00, 80.11it/s, loss=0.278]
+Epoch:  95%|| 954/1000 [00:12<00:00, 80.11it/s, loss=0.278]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.278]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.278]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.277]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.278]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.277]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.278]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.277]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.278]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.278]
+Epoch:  96%|| 963/1000 [00:12<00:00, 82.41it/s, loss=0.278]
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.278]
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.279]
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.279]
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.279]
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.28] 
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.279]
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.28] 
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.28]
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.28]
+Epoch:  97%|| 972/1000 [00:12<00:00, 83.89it/s, loss=0.28]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.28]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.28]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.281]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.281]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.281]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.281]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.281]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.281]
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.28] 
+Epoch:  98%|| 981/1000 [00:12<00:00, 83.82it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.28]
+Epoch:  99%|| 990/1000 [00:12<00:00, 77.03it/s, loss=0.279]
+Epoch: 100%|| 999/1000 [00:12<00:00, 79.77it/s, loss=0.279]
+Epoch: 100%|| 999/1000 [00:12<00:00, 79.77it/s, loss=0.28] 
+Epoch: 100%|| 1000/1000 [00:12<00:00, 80.16it/s, loss=0.28]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin1] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.936]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.931]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.896]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.849]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.813]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.813]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.78] 
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.763]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.74] 
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.724]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.707]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.691]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.679]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 69.69it/s, loss=0.669]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.669]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.656]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.647]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.642]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.632]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.624]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.613]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.61] 
+Epoch:   2%|         | 15/1000 [00:00<00:13, 74.96it/s, loss=0.604]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.604]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.596]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.592]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.587]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.582]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.577]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.569]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.566]
+Epoch:   2%|         | 23/1000 [00:00<00:12, 76.02it/s, loss=0.562]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.562]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.557]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.552]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.549]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.544]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.54] 
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.536]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.533]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.529]
+Epoch:   3%|         | 31/1000 [00:00<00:12, 75.82it/s, loss=0.527]
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+Epoch:   4%|         | 40/1000 [00:00<00:12, 78.94it/s, loss=0.523]
+Epoch:   4%|         | 40/1000 [00:00<00:12, 78.94it/s, loss=0.52] 
+Epoch:   4%|         | 40/1000 [00:00<00:12, 78.94it/s, loss=0.516]
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+Epoch:   4%|         | 40/1000 [00:00<00:12, 78.94it/s, loss=0.501]
+Epoch:   4%|         | 40/1000 [00:00<00:12, 78.94it/s, loss=0.499]
+Epoch:   5%|         | 49/1000 [00:00<00:11, 81.57it/s, loss=0.499]
+Epoch:   5%|         | 49/1000 [00:00<00:11, 81.57it/s, loss=0.498]
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+Epoch:   5%|         | 49/1000 [00:00<00:11, 81.57it/s, loss=0.487]
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+Epoch:   6%|         | 59/1000 [00:00<00:11, 85.29it/s, loss=0.476]
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+Epoch:   7%|         | 68/1000 [00:00<00:12, 75.49it/s, loss=0.458]
+Epoch:   7%|         | 68/1000 [00:00<00:12, 75.49it/s, loss=0.457]
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+Epoch:   7%|         | 68/1000 [00:00<00:12, 75.49it/s, loss=0.453]
+Epoch:   7%|         | 68/1000 [00:00<00:12, 75.49it/s, loss=0.453]
+Epoch:   7%|         | 68/1000 [00:01<00:12, 75.49it/s, loss=0.451]
+Epoch:   8%|         | 76/1000 [00:01<00:13, 69.72it/s, loss=0.451]
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+Epoch:   8%|         | 84/1000 [00:01<00:13, 69.23it/s, loss=0.437]
+Epoch:   8%|         | 84/1000 [00:01<00:13, 69.23it/s, loss=0.436]
+Epoch:   8%|         | 84/1000 [00:01<00:13, 69.23it/s, loss=0.436]
+Epoch:   8%|         | 84/1000 [00:01<00:13, 69.23it/s, loss=0.435]
+Epoch:   8%|         | 84/1000 [00:01<00:13, 69.23it/s, loss=0.434]
+Epoch:   8%|         | 84/1000 [00:01<00:13, 69.23it/s, loss=0.433]
+Epoch:   8%|         | 84/1000 [00:01<00:13, 69.23it/s, loss=0.431]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.431]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.43] 
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.429]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.428]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.427]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.426]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.425]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.423]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.423]
+Epoch:   9%|         | 92/1000 [00:01<00:12, 71.61it/s, loss=0.295]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.295]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.327]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.311]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.308]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.317]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.314]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.315]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.314]
+Epoch:  10%|         | 101/1000 [00:01<00:11, 75.25it/s, loss=0.32] 
+Epoch:  11%|         | 109/1000 [00:01<00:11, 75.74it/s, loss=0.32]
+Epoch:  11%|         | 109/1000 [00:01<00:11, 75.74it/s, loss=0.321]
+Epoch:  11%|         | 109/1000 [00:01<00:11, 75.74it/s, loss=0.32] 
+Epoch:  11%|         | 109/1000 [00:01<00:11, 75.74it/s, loss=0.319]
+Epoch:  11%|         | 109/1000 [00:01<00:11, 75.74it/s, loss=0.319]
+Epoch:  11%|         | 109/1000 [00:01<00:11, 75.74it/s, loss=0.325]
+Epoch:  11%|         | 109/1000 [00:01<00:11, 75.74it/s, loss=0.328]
+Epoch:  11%|         | 109/1000 [00:01<00:11, 75.74it/s, loss=0.328]
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+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.325]
+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.324]
+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.323]
+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.323]
+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.323]
+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.322]
+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.322]
+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.324]
+Epoch:  12%|        | 118/1000 [00:01<00:11, 78.54it/s, loss=0.323]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.323]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.322]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.32] 
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.318]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.32] 
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.321]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.321]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.321]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.321]
+Epoch:  13%|        | 126/1000 [00:01<00:11, 78.56it/s, loss=0.321]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.321]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.32] 
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.32]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.321]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.322]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.321]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.321]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.32] 
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.319]
+Epoch:  14%|        | 135/1000 [00:01<00:10, 81.39it/s, loss=0.32] 
+Epoch:  14%|        | 144/1000 [00:01<00:10, 80.35it/s, loss=0.32]
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+Epoch:  14%|        | 144/1000 [00:01<00:10, 80.35it/s, loss=0.319]
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+Epoch:  16%|        | 162/1000 [00:02<00:10, 82.07it/s, loss=0.318]
+Epoch:  16%|        | 162/1000 [00:02<00:10, 82.07it/s, loss=0.318]
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+Epoch:  17%|        | 171/1000 [00:02<00:10, 80.79it/s, loss=0.318]
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+Epoch:  20%|        | 204/1000 [00:02<00:11, 66.44it/s, loss=0.336]
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+Epoch:  20%|        | 204/1000 [00:02<00:11, 66.44it/s, loss=0.326]
+Epoch:  21%|        | 211/1000 [00:02<00:13, 59.52it/s, loss=0.326]
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+Epoch:  23%|       | 226/1000 [00:03<00:12, 62.41it/s, loss=0.321]
+Epoch:  23%|       | 226/1000 [00:03<00:12, 62.41it/s, loss=0.321]
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+Epoch:  26%|       | 261/1000 [00:03<00:09, 78.14it/s, loss=0.311]
+Epoch:  26%|       | 261/1000 [00:03<00:09, 78.14it/s, loss=0.311]
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+Epoch:  27%|       | 269/1000 [00:03<00:09, 77.83it/s, loss=0.311]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 77.83it/s, loss=0.312]
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+Epoch:  27%|       | 269/1000 [00:03<00:09, 77.83it/s, loss=0.313]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 77.83it/s, loss=0.312]
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+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.314]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.314]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.314]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.314]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.313]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.313]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.313]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.312]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.312]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 80.56it/s, loss=0.312]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.312]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.312]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:09, 78.33it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 80.26it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 80.26it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 80.26it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 80.26it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:04<00:08, 80.26it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:04<00:08, 80.26it/s, loss=0.302]
+Epoch:  30%|       | 296/1000 [00:04<00:08, 80.26it/s, loss=0.318]
+Epoch:  30%|       | 296/1000 [00:04<00:08, 80.26it/s, loss=0.31] 
+Epoch:  30%|       | 296/1000 [00:04<00:08, 80.26it/s, loss=0.301]
+Epoch:  30%|       | 296/1000 [00:04<00:08, 80.26it/s, loss=0.295]
+Epoch:  30%|       | 296/1000 [00:04<00:08, 80.26it/s, loss=0.296]
+Epoch:  31%|       | 306/1000 [00:04<00:08, 83.91it/s, loss=0.296]
+Epoch:  31%|       | 306/1000 [00:04<00:08, 83.91it/s, loss=0.305]
+Epoch:  31%|       | 306/1000 [00:04<00:08, 83.91it/s, loss=0.304]
+Epoch:  31%|       | 306/1000 [00:04<00:08, 83.91it/s, loss=0.309]
+Epoch:  31%|       | 306/1000 [00:04<00:08, 83.91it/s, loss=0.306]
+Epoch:  31%|       | 306/1000 [00:04<00:08, 83.91it/s, loss=0.315]
+Epoch:  31%|       | 306/1000 [00:04<00:08, 83.91it/s, loss=0.31] 
+Epoch:  31%|       | 306/1000 [00:04<00:08, 83.91it/s, loss=0.312]
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+Epoch:  32%|      | 315/1000 [00:04<00:08, 81.43it/s, loss=0.311]
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+Epoch:  32%|      | 324/1000 [00:04<00:08, 81.99it/s, loss=0.311]
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+Epoch:  32%|      | 324/1000 [00:04<00:08, 81.99it/s, loss=0.308]
+Epoch:  32%|      | 324/1000 [00:04<00:08, 81.99it/s, loss=0.306]
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+Epoch:  32%|      | 324/1000 [00:04<00:08, 81.99it/s, loss=0.307]
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+Epoch:  33%|      | 333/1000 [00:04<00:08, 82.76it/s, loss=0.304]
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+Epoch:  44%|     | 440/1000 [00:05<00:07, 79.39it/s, loss=0.316]
+Epoch:  44%|     | 440/1000 [00:05<00:07, 79.39it/s, loss=0.316]
+Epoch:  44%|     | 440/1000 [00:05<00:07, 79.39it/s, loss=0.317]
+Epoch:  44%|     | 440/1000 [00:05<00:07, 79.39it/s, loss=0.317]
+Epoch:  44%|     | 440/1000 [00:05<00:07, 79.39it/s, loss=0.316]
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+Epoch:  45%|     | 449/1000 [00:05<00:06, 80.74it/s, loss=0.315]
+Epoch:  45%|     | 449/1000 [00:05<00:06, 80.74it/s, loss=0.315]
+Epoch:  45%|     | 449/1000 [00:05<00:06, 80.74it/s, loss=0.316]
+Epoch:  45%|     | 449/1000 [00:05<00:06, 80.74it/s, loss=0.315]
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+Epoch:  45%|     | 449/1000 [00:05<00:06, 80.74it/s, loss=0.314]
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+Epoch:  47%|     | 467/1000 [00:06<00:06, 81.90it/s, loss=0.31]
+Epoch:  47%|     | 467/1000 [00:06<00:06, 81.90it/s, loss=0.31]
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+Epoch:  47%|     | 467/1000 [00:06<00:06, 81.90it/s, loss=0.311]
+Epoch:  47%|     | 467/1000 [00:06<00:06, 81.90it/s, loss=0.31] 
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+Epoch:  48%|     | 485/1000 [00:06<00:06, 80.35it/s, loss=0.309]
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+Epoch:  49%|     | 494/1000 [00:06<00:06, 80.02it/s, loss=0.309]
+Epoch:  49%|     | 494/1000 [00:06<00:06, 80.02it/s, loss=0.309]
+Epoch:  49%|     | 494/1000 [00:06<00:06, 80.02it/s, loss=0.309]
+Epoch:  49%|     | 494/1000 [00:06<00:06, 80.02it/s, loss=0.309]
+Epoch:  49%|     | 494/1000 [00:06<00:06, 80.02it/s, loss=0.309]
+Epoch:  49%|     | 494/1000 [00:06<00:06, 80.02it/s, loss=0.308]
+Epoch:  49%|     | 494/1000 [00:06<00:06, 80.02it/s, loss=0.308]
+Epoch:  49%|     | 494/1000 [00:06<00:06, 80.02it/s, loss=0.322]
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+Epoch:  81%|  | 809/1000 [00:10<00:02, 74.01it/s, loss=0.285]
+Epoch:  81%|  | 809/1000 [00:10<00:02, 74.01it/s, loss=0.287]
+Epoch:  81%|  | 809/1000 [00:10<00:02, 74.01it/s, loss=0.288]
+Epoch:  81%|  | 809/1000 [00:10<00:02, 74.01it/s, loss=0.29] 
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.29]
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.288]
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.29] 
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.288]
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.287]
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.287]
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.284]
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.284]
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.287]
+Epoch:  82%| | 817/1000 [00:10<00:02, 75.26it/s, loss=0.288]
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.288]
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.29] 
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.291]
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.292]
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.293]
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.29] 
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.29]
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.291]
+Epoch:  83%| | 826/1000 [00:10<00:02, 77.72it/s, loss=0.292]
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.292]
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.292]
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.292]
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.291]
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.291]
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.291]
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.291]
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.29] 
+Epoch:  83%| | 834/1000 [00:10<00:02, 76.89it/s, loss=0.291]
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.291]
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.291]
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.29] 
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.291]
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.291]
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.293]
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.293]
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.293]
+Epoch:  84%| | 842/1000 [00:10<00:02, 75.32it/s, loss=0.293]
+Epoch:  85%| | 850/1000 [00:10<00:02, 72.71it/s, loss=0.293]
+Epoch:  85%| | 850/1000 [00:10<00:02, 72.71it/s, loss=0.293]
+Epoch:  85%| | 850/1000 [00:10<00:02, 72.71it/s, loss=0.294]
+Epoch:  85%| | 850/1000 [00:10<00:02, 72.71it/s, loss=0.293]
+Epoch:  85%| | 850/1000 [00:11<00:02, 72.71it/s, loss=0.292]
+Epoch:  85%| | 850/1000 [00:11<00:02, 72.71it/s, loss=0.294]
+Epoch:  85%| | 850/1000 [00:11<00:02, 72.71it/s, loss=0.294]
+Epoch:  85%| | 850/1000 [00:11<00:02, 72.71it/s, loss=0.293]
+Epoch:  85%| | 850/1000 [00:11<00:02, 72.71it/s, loss=0.294]
+Epoch:  85%| | 850/1000 [00:11<00:02, 72.71it/s, loss=0.294]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.294]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.293]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.293]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.293]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.292]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.292]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.292]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.292]
+Epoch:  86%| | 859/1000 [00:11<00:01, 75.97it/s, loss=0.291]
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.291]
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.291]
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.291]
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.29] 
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.291]
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.291]
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.29] 
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.291]
+Epoch:  87%| | 867/1000 [00:11<00:01, 77.07it/s, loss=0.29] 
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.29]
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.29]
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.29]
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.29]
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.289]
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.289]
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.289]
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.289]
+Epoch:  88%| | 875/1000 [00:11<00:01, 76.36it/s, loss=0.29] 
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.29]
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.289]
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.289]
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.289]
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.289]
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.29] 
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.29]
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.29]
+Epoch:  88%| | 883/1000 [00:11<00:01, 76.10it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.29]
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.289]
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.289]
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.29] 
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.289]
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.29] 
+Epoch:  89%| | 891/1000 [00:11<00:01, 74.37it/s, loss=0.29]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.29]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.29]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.343]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.3]  
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.3]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.284]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.284]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.286]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.286]
+Epoch:  90%| | 899/1000 [00:11<00:01, 74.57it/s, loss=0.289]
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.289]
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.284]
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.287]
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.288]
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.289]
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.29] 
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.291]
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.29] 
+Epoch:  91%| | 908/1000 [00:11<00:01, 76.82it/s, loss=0.29]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.29]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.293]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.289]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.289]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.286]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.286]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.289]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.286]
+Epoch:  92%|| 916/1000 [00:11<00:01, 76.75it/s, loss=0.287]
+Epoch:  92%|| 924/1000 [00:11<00:01, 75.63it/s, loss=0.287]
+Epoch:  92%|| 924/1000 [00:11<00:01, 75.63it/s, loss=0.287]
+Epoch:  92%|| 924/1000 [00:11<00:01, 75.63it/s, loss=0.287]
+Epoch:  92%|| 924/1000 [00:11<00:01, 75.63it/s, loss=0.287]
+Epoch:  92%|| 924/1000 [00:11<00:01, 75.63it/s, loss=0.288]
+Epoch:  92%|| 924/1000 [00:11<00:01, 75.63it/s, loss=0.287]
+Epoch:  92%|| 924/1000 [00:12<00:01, 75.63it/s, loss=0.286]
+Epoch:  92%|| 924/1000 [00:12<00:01, 75.63it/s, loss=0.284]
+Epoch:  92%|| 924/1000 [00:12<00:01, 75.63it/s, loss=0.282]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.282]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.282]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.281]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.281]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.281]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.281]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.281]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.28] 
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.28]
+Epoch:  93%|| 932/1000 [00:12<00:00, 76.43it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.277]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  94%|| 941/1000 [00:12<00:00, 78.03it/s, loss=0.278]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.278]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.277]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.276]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.277]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.277]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.277]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.277]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.277]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.277]
+Epoch:  95%|| 950/1000 [00:12<00:00, 78.16it/s, loss=0.278]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.278]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.279]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.279]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.278]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.278]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.278]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.277]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.278]
+Epoch:  96%|| 959/1000 [00:12<00:00, 79.26it/s, loss=0.277]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.277]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.278]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.277]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.278]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.278]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.278]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.279]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.279]
+Epoch:  97%|| 967/1000 [00:12<00:00, 77.15it/s, loss=0.279]
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.279]
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.28] 
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.279]
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.28] 
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.28]
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.28]
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.28]
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.28]
+Epoch:  98%|| 975/1000 [00:12<00:00, 73.70it/s, loss=0.281]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.281]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.281]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.281]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.281]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.281]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.281]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.28] 
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.28]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.28]
+Epoch:  98%|| 983/1000 [00:12<00:00, 75.37it/s, loss=0.28]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.28]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.28]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.28]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.28]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.28]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.28]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.28]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.279]
+Epoch:  99%|| 992/1000 [00:12<00:00, 78.67it/s, loss=0.28] 
+Epoch: 100%|| 1000/1000 [00:12<00:00, 77.69it/s, loss=0.28]
+Epoch: 100%|| 1000/1000 [00:12<00:00, 77.49it/s, loss=0.28]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin2] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=1.03]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.951]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.936]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.931]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.896]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.849]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=0.813]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 67.07it/s, loss=0.813]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 67.07it/s, loss=0.78] 
+Epoch:   1%|          | 7/1000 [00:00<00:14, 67.07it/s, loss=0.763]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 67.07it/s, loss=0.74] 
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+Epoch:   1%|          | 7/1000 [00:00<00:14, 67.07it/s, loss=0.679]
+Epoch:   1%|          | 7/1000 [00:00<00:14, 67.07it/s, loss=0.669]
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+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.656]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.647]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.642]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.632]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.624]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.613]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.61] 
+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.604]
+Epoch:   2%|         | 15/1000 [00:00<00:13, 72.20it/s, loss=0.596]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 77.97it/s, loss=0.596]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 77.97it/s, loss=0.592]
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+Epoch:   2%|         | 24/1000 [00:00<00:12, 77.97it/s, loss=0.557]
+Epoch:   2%|         | 24/1000 [00:00<00:12, 77.97it/s, loss=0.552]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.552]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.549]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.544]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.54] 
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.536]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.533]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.529]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.527]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.523]
+Epoch:   3%|         | 33/1000 [00:00<00:12, 80.15it/s, loss=0.52] 
+Epoch:   4%|         | 42/1000 [00:00<00:11, 80.63it/s, loss=0.52]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 80.63it/s, loss=0.516]
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+Epoch:   4%|         | 42/1000 [00:00<00:11, 80.63it/s, loss=0.507]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 80.63it/s, loss=0.504]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 80.63it/s, loss=0.501]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 80.63it/s, loss=0.499]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 80.63it/s, loss=0.498]
+Epoch:   4%|         | 42/1000 [00:00<00:11, 80.63it/s, loss=0.495]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.495]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.493]
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+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.487]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.487]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.485]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.482]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.481]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.479]
+Epoch:   5%|         | 51/1000 [00:00<00:11, 79.86it/s, loss=0.476]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.476]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.474]
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+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.47]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.468]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.466]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.463]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.462]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.461]
+Epoch:   6%|         | 60/1000 [00:00<00:11, 82.67it/s, loss=0.46] 
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.46]
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.458]
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.457]
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.455]
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.453]
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.453]
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.451]
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.45] 
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.449]
+Epoch:   7%|         | 70/1000 [00:00<00:10, 85.47it/s, loss=0.447]
+Epoch:   8%|         | 79/1000 [00:00<00:10, 84.12it/s, loss=0.447]
+Epoch:   8%|         | 79/1000 [00:00<00:10, 84.12it/s, loss=0.447]
+Epoch:   8%|         | 79/1000 [00:00<00:10, 84.12it/s, loss=0.445]
+Epoch:   8%|         | 79/1000 [00:01<00:10, 84.12it/s, loss=0.443]
+Epoch:   8%|         | 79/1000 [00:01<00:10, 84.12it/s, loss=0.442]
+Epoch:   8%|         | 79/1000 [00:01<00:10, 84.12it/s, loss=0.441]
+Epoch:   8%|         | 79/1000 [00:01<00:10, 84.12it/s, loss=0.439]
+Epoch:   8%|         | 79/1000 [00:01<00:10, 84.12it/s, loss=0.437]
+Epoch:   8%|         | 79/1000 [00:01<00:10, 84.12it/s, loss=0.436]
+Epoch:   8%|         | 79/1000 [00:01<00:10, 84.12it/s, loss=0.436]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.436]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.435]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.434]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.433]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.431]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.43] 
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.429]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.428]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.427]
+Epoch:   9%|         | 88/1000 [00:01<00:10, 84.01it/s, loss=0.426]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.426]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.425]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.423]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.423]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.295]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.327]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.311]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.308]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.317]
+Epoch:  10%|         | 97/1000 [00:01<00:10, 85.00it/s, loss=0.314]
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.314]
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.315]
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.314]
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.32] 
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.321]
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.32] 
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.319]
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.319]
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.325]
+Epoch:  11%|         | 106/1000 [00:01<00:10, 86.00it/s, loss=0.328]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.328]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.328]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.324]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.325]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.324]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.323]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.323]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.323]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.322]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.322]
+Epoch:  12%|        | 115/1000 [00:01<00:10, 85.85it/s, loss=0.324]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.324]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.323]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.322]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.32] 
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.318]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.32] 
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.321]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.321]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.321]
+Epoch:  12%|        | 125/1000 [00:01<00:10, 87.15it/s, loss=0.321]
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.321]
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.321]
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.32] 
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.32]
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.321]
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.322]
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.321]
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.321]
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.32] 
+Epoch:  13%|        | 134/1000 [00:01<00:10, 86.03it/s, loss=0.319]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.319]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.32] 
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.319]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.319]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.319]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.319]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.318]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.319]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.319]
+Epoch:  14%|        | 143/1000 [00:01<00:10, 82.54it/s, loss=0.321]
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+Epoch:  16%|        | 161/1000 [00:01<00:09, 84.60it/s, loss=0.318]
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+Epoch:  17%|        | 170/1000 [00:02<00:09, 83.75it/s, loss=0.318]
+Epoch:  17%|        | 170/1000 [00:02<00:09, 83.75it/s, loss=0.317]
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+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.318]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.317]
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+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.317]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.318]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.318]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.317]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.317]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.317]
+Epoch:  18%|        | 179/1000 [00:02<00:10, 77.27it/s, loss=0.316]
+Epoch:  19%|        | 188/1000 [00:02<00:10, 80.53it/s, loss=0.316]
+Epoch:  19%|        | 188/1000 [00:02<00:10, 80.53it/s, loss=0.317]
+Epoch:  19%|        | 188/1000 [00:02<00:10, 80.53it/s, loss=0.316]
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+Epoch:  19%|        | 188/1000 [00:02<00:10, 80.53it/s, loss=0.317]
+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.317]
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+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.316]
+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.316]
+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.269]
+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.347]
+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.349]
+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.342]
+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.34] 
+Epoch:  20%|        | 197/1000 [00:02<00:10, 80.25it/s, loss=0.343]
+Epoch:  21%|        | 206/1000 [00:02<00:09, 81.13it/s, loss=0.343]
+Epoch:  21%|        | 206/1000 [00:02<00:09, 81.13it/s, loss=0.336]
+Epoch:  21%|        | 206/1000 [00:02<00:09, 81.13it/s, loss=0.339]
+Epoch:  21%|        | 206/1000 [00:02<00:09, 81.13it/s, loss=0.336]
+Epoch:  21%|        | 206/1000 [00:02<00:09, 81.13it/s, loss=0.326]
+Epoch:  21%|        | 206/1000 [00:02<00:09, 81.13it/s, loss=0.326]
+Epoch:  21%|        | 206/1000 [00:02<00:09, 81.13it/s, loss=0.326]
+Epoch:  21%|        | 206/1000 [00:02<00:09, 81.13it/s, loss=0.332]
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+Epoch:  22%|       | 215/1000 [00:02<00:09, 83.47it/s, loss=0.329]
+Epoch:  22%|       | 215/1000 [00:02<00:09, 83.47it/s, loss=0.328]
+Epoch:  22%|       | 215/1000 [00:02<00:09, 83.47it/s, loss=0.327]
+Epoch:  22%|       | 215/1000 [00:02<00:09, 83.47it/s, loss=0.324]
+Epoch:  22%|       | 215/1000 [00:02<00:09, 83.47it/s, loss=0.325]
+Epoch:  22%|       | 215/1000 [00:02<00:09, 83.47it/s, loss=0.324]
+Epoch:  22%|       | 224/1000 [00:02<00:09, 82.89it/s, loss=0.324]
+Epoch:  22%|       | 224/1000 [00:02<00:09, 82.89it/s, loss=0.322]
+Epoch:  22%|       | 224/1000 [00:02<00:09, 82.89it/s, loss=0.321]
+Epoch:  22%|       | 224/1000 [00:02<00:09, 82.89it/s, loss=0.321]
+Epoch:  22%|       | 224/1000 [00:02<00:09, 82.89it/s, loss=0.321]
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+Epoch:  24%|       | 242/1000 [00:03<00:09, 83.82it/s, loss=0.313]
+Epoch:  24%|       | 242/1000 [00:03<00:09, 83.82it/s, loss=0.313]
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+Epoch:  26%|       | 260/1000 [00:03<00:08, 84.15it/s, loss=0.311]
+Epoch:  26%|       | 260/1000 [00:03<00:08, 84.15it/s, loss=0.311]
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+Epoch:  26%|       | 260/1000 [00:03<00:08, 84.15it/s, loss=0.311]
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+Epoch:  27%|       | 269/1000 [00:03<00:09, 80.83it/s, loss=0.311]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 80.83it/s, loss=0.312]
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+Epoch:  27%|       | 269/1000 [00:03<00:09, 80.83it/s, loss=0.313]
+Epoch:  27%|       | 269/1000 [00:03<00:09, 80.83it/s, loss=0.312]
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+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.314]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.314]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.314]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.313]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.313]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.313]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.312]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.312]
+Epoch:  28%|       | 278/1000 [00:03<00:08, 81.44it/s, loss=0.312]
+Epoch:  29%|       | 287/1000 [00:03<00:08, 81.00it/s, loss=0.312]
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+Epoch:  29%|       | 287/1000 [00:03<00:08, 81.00it/s, loss=0.313]
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+Epoch:  29%|       | 287/1000 [00:03<00:08, 81.00it/s, loss=0.313]
+Epoch:  29%|       | 287/1000 [00:03<00:08, 81.00it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.314]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.302]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.318]
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+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.301]
+Epoch:  30%|       | 296/1000 [00:03<00:08, 81.42it/s, loss=0.295]
+Epoch:  30%|       | 305/1000 [00:03<00:08, 81.00it/s, loss=0.295]
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+Epoch:  30%|       | 305/1000 [00:03<00:08, 81.00it/s, loss=0.312]
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+Epoch:  40%|      | 405/1000 [00:04<00:07, 80.48it/s, loss=0.315]
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+Epoch:  44%|     | 441/1000 [00:05<00:06, 81.74it/s, loss=0.316]
+Epoch:  44%|     | 441/1000 [00:05<00:06, 81.74it/s, loss=0.317]
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+Epoch:  44%|     | 441/1000 [00:05<00:06, 81.74it/s, loss=0.315]
+Epoch:  45%|     | 450/1000 [00:05<00:06, 83.26it/s, loss=0.315]
+Epoch:  45%|     | 450/1000 [00:05<00:06, 83.26it/s, loss=0.315]
+Epoch:  45%|     | 450/1000 [00:05<00:06, 83.26it/s, loss=0.316]
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+Epoch:  45%|     | 450/1000 [00:05<00:06, 83.26it/s, loss=0.314]
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+Epoch:  46%|     | 459/1000 [00:05<00:06, 83.39it/s, loss=0.313]
+Epoch:  46%|     | 459/1000 [00:05<00:06, 83.39it/s, loss=0.313]
+Epoch:  46%|     | 459/1000 [00:05<00:06, 83.39it/s, loss=0.313]
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+Epoch:  46%|     | 459/1000 [00:05<00:06, 83.39it/s, loss=0.311]
+Epoch:  46%|     | 459/1000 [00:05<00:06, 83.39it/s, loss=0.31] 
+Epoch:  46%|     | 459/1000 [00:05<00:06, 83.39it/s, loss=0.31]
+Epoch:  46%|     | 459/1000 [00:05<00:06, 83.39it/s, loss=0.31]
+Epoch:  46%|     | 459/1000 [00:05<00:06, 83.39it/s, loss=0.31]
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+Epoch:  47%|     | 468/1000 [00:05<00:06, 80.62it/s, loss=0.31]
+Epoch:  47%|     | 468/1000 [00:05<00:06, 80.62it/s, loss=0.31]
+Epoch:  47%|     | 468/1000 [00:05<00:06, 80.62it/s, loss=0.311]
+Epoch:  47%|     | 468/1000 [00:05<00:06, 80.62it/s, loss=0.31] 
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+Epoch:  99%|| 986/1000 [00:12<00:00, 74.28it/s, loss=0.28]
+Epoch:  99%|| 986/1000 [00:12<00:00, 74.28it/s, loss=0.28]
+Epoch:  99%|| 986/1000 [00:12<00:00, 74.28it/s, loss=0.28]
+Epoch:  99%|| 986/1000 [00:12<00:00, 74.28it/s, loss=0.28]
+Epoch:  99%|| 986/1000 [00:12<00:00, 74.28it/s, loss=0.28]
+Epoch:  99%|| 986/1000 [00:12<00:00, 74.28it/s, loss=0.28]
+Epoch: 100%|| 995/1000 [00:12<00:00, 77.13it/s, loss=0.28]
+Epoch: 100%|| 995/1000 [00:12<00:00, 77.13it/s, loss=0.28]
+Epoch: 100%|| 995/1000 [00:12<00:00, 77.13it/s, loss=0.28]
+Epoch: 100%|| 995/1000 [00:12<00:00, 77.13it/s, loss=0.28]
+Epoch: 100%|| 995/1000 [00:12<00:00, 77.13it/s, loss=0.279]
+Epoch: 100%|| 995/1000 [00:12<00:00, 77.13it/s, loss=0.28] 
+Epoch: 100%|| 1000/1000 [00:12<00:00, 80.63it/s, loss=0.28]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin3] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=2.88]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=6.85]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=6.85]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=5.44]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=4.72]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=4.27]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=3.92]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=3.69]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=3.46]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=3.28]
+Epoch:   0%|          | 2/1000 [00:00<00:54, 18.30it/s, loss=3.2] 
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=3.2]
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=3.14]
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=3.05]
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=2.96]
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=2.95]
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=2.88]
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=2.8] 
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=2.77]
+Epoch:   1%|          | 10/1000 [00:00<00:19, 50.83it/s, loss=2.73]
+Epoch:   2%|         | 18/1000 [00:00<00:16, 60.62it/s, loss=2.73]
+Epoch:   2%|         | 18/1000 [00:00<00:16, 60.62it/s, loss=2.72]
+Epoch:   2%|         | 18/1000 [00:00<00:16, 60.62it/s, loss=2.7] 
+Epoch:   2%|         | 18/1000 [00:00<00:16, 60.62it/s, loss=2.65]
+Epoch:   2%|         | 18/1000 [00:00<00:16, 60.62it/s, loss=2.62]
+Epoch:   2%|         | 18/1000 [00:00<00:16, 60.62it/s, loss=2.62]
+Epoch:   2%|         | 18/1000 [00:00<00:16, 60.62it/s, loss=2.59]
+Epoch:   2%|         | 18/1000 [00:00<00:16, 60.62it/s, loss=2.57]
+Epoch:   2%|         | 25/1000 [00:00<00:15, 61.94it/s, loss=2.57]
+Epoch:   2%|         | 25/1000 [00:00<00:15, 61.94it/s, loss=2.57]
+Epoch:   2%|         | 25/1000 [00:00<00:15, 61.94it/s, loss=2.54]
+Epoch:   2%|         | 25/1000 [00:00<00:15, 61.94it/s, loss=2.52]
+Epoch:   2%|         | 25/1000 [00:00<00:15, 61.94it/s, loss=2.51]
+Epoch:   2%|         | 25/1000 [00:00<00:15, 61.94it/s, loss=2.5] 
+Epoch:   2%|         | 25/1000 [00:00<00:15, 61.94it/s, loss=2.49]
+Epoch:   2%|         | 25/1000 [00:00<00:15, 61.94it/s, loss=2.48]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.48]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.47]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.46]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.58]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.57]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.55]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.54]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.54]
+Epoch:   3%|         | 32/1000 [00:00<00:15, 63.00it/s, loss=2.52]
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.52]
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.51]
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.5] 
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.49]
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.48]
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.48]
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.46]
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.47]
+Epoch:   4%|         | 40/1000 [00:00<00:14, 65.87it/s, loss=2.46]
+Epoch:   5%|         | 48/1000 [00:00<00:13, 68.31it/s, loss=2.46]
+Epoch:   5%|         | 48/1000 [00:00<00:13, 68.31it/s, loss=2.45]
+Epoch:   5%|         | 48/1000 [00:00<00:13, 68.31it/s, loss=2.43]
+Epoch:   5%|         | 48/1000 [00:00<00:13, 68.31it/s, loss=2.42]
+Epoch:   5%|         | 48/1000 [00:00<00:13, 68.31it/s, loss=2.42]
+Epoch:   5%|         | 48/1000 [00:00<00:13, 68.31it/s, loss=2.41]
+Epoch:   5%|         | 48/1000 [00:00<00:13, 68.31it/s, loss=2.4] 
+Epoch:   5%|         | 48/1000 [00:00<00:13, 68.31it/s, loss=2.4]
+Epoch:   6%|         | 55/1000 [00:00<00:14, 64.73it/s, loss=2.4]
+Epoch:   6%|         | 55/1000 [00:00<00:14, 64.73it/s, loss=2.39]
+Epoch:   6%|         | 55/1000 [00:00<00:14, 64.73it/s, loss=2.39]
+Epoch:   6%|         | 55/1000 [00:00<00:14, 64.73it/s, loss=2.41]
+Epoch:   6%|         | 55/1000 [00:00<00:14, 64.73it/s, loss=2.41]
+Epoch:   6%|         | 55/1000 [00:00<00:14, 64.73it/s, loss=2.41]
+Epoch:   6%|         | 55/1000 [00:00<00:14, 64.73it/s, loss=2.4] 
+Epoch:   6%|         | 55/1000 [00:00<00:14, 64.73it/s, loss=2.39]
+Epoch:   6%|         | 62/1000 [00:00<00:14, 64.43it/s, loss=2.39]
+Epoch:   6%|         | 62/1000 [00:01<00:14, 64.43it/s, loss=2.4] 
+Epoch:   6%|         | 62/1000 [00:01<00:14, 64.43it/s, loss=2.4]
+Epoch:   6%|         | 62/1000 [00:01<00:14, 64.43it/s, loss=2.39]
+Epoch:   6%|         | 62/1000 [00:01<00:14, 64.43it/s, loss=2.39]
+Epoch:   6%|         | 62/1000 [00:01<00:14, 64.43it/s, loss=2.4] 
+Epoch:   6%|         | 62/1000 [00:01<00:14, 64.43it/s, loss=2.4]
+Epoch:   6%|         | 62/1000 [00:01<00:14, 64.43it/s, loss=2.39]
+Epoch:   6%|         | 62/1000 [00:01<00:14, 64.43it/s, loss=2.38]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.38]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.37]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.37]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.36]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.36]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.37]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.36]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.36]
+Epoch:   7%|         | 70/1000 [00:01<00:13, 66.92it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 69.53it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 69.53it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 69.53it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 69.53it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 69.53it/s, loss=2.35]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 69.53it/s, loss=2.35]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 69.53it/s, loss=2.34]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 69.53it/s, loss=2.33]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 66.47it/s, loss=2.33]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 66.47it/s, loss=2.33]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 66.47it/s, loss=2.32]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 66.47it/s, loss=2.32]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 66.47it/s, loss=2.31]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 66.47it/s, loss=2.31]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 66.47it/s, loss=2.31]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 66.47it/s, loss=2.3] 
+Epoch:   9%|         | 92/1000 [00:01<00:13, 67.10it/s, loss=2.3]
+Epoch:   9%|         | 92/1000 [00:01<00:13, 67.10it/s, loss=2.3]
+Epoch:   9%|         | 92/1000 [00:01<00:13, 67.10it/s, loss=2.3]
+Epoch:   9%|         | 92/1000 [00:01<00:13, 67.10it/s, loss=2.29]
+Epoch:   9%|         | 92/1000 [00:01<00:13, 67.10it/s, loss=2.29]
+Epoch:   9%|         | 92/1000 [00:01<00:13, 67.10it/s, loss=2.29]
+Epoch:   9%|         | 92/1000 [00:01<00:13, 67.10it/s, loss=2.28]
+Epoch:   9%|         | 92/1000 [00:01<00:13, 67.10it/s, loss=2.28]
+Epoch:  10%|         | 99/1000 [00:01<00:14, 63.18it/s, loss=2.28]
+Epoch:  10%|         | 99/1000 [00:01<00:14, 63.18it/s, loss=2.28]
+Epoch:  10%|         | 99/1000 [00:01<00:14, 63.18it/s, loss=1.53]
+Epoch:  10%|         | 99/1000 [00:01<00:14, 63.18it/s, loss=1.87]
+Epoch:  10%|         | 99/1000 [00:01<00:14, 63.18it/s, loss=2.01]
+Epoch:  10%|         | 99/1000 [00:01<00:14, 63.18it/s, loss=2.11]
+Epoch:  10%|         | 99/1000 [00:01<00:14, 63.18it/s, loss=2.1] 
+Epoch:  10%|         | 99/1000 [00:01<00:14, 63.18it/s, loss=2.05]
+Epoch:  11%|         | 106/1000 [00:01<00:14, 60.84it/s, loss=2.05]
+Epoch:  11%|         | 106/1000 [00:01<00:14, 60.84it/s, loss=1.98]
+Epoch:  11%|         | 106/1000 [00:01<00:14, 60.84it/s, loss=1.97]
+Epoch:  11%|         | 106/1000 [00:01<00:14, 60.84it/s, loss=1.93]
+Epoch:  11%|         | 106/1000 [00:01<00:14, 60.84it/s, loss=1.89]
+Epoch:  11%|         | 106/1000 [00:01<00:14, 60.84it/s, loss=1.89]
+Epoch:  11%|         | 106/1000 [00:01<00:14, 60.84it/s, loss=1.92]
+Epoch:  11%|         | 106/1000 [00:01<00:14, 60.84it/s, loss=1.9] 
+Epoch:  11%|        | 113/1000 [00:01<00:14, 61.11it/s, loss=1.9]
+Epoch:  11%|        | 113/1000 [00:01<00:14, 61.11it/s, loss=1.88]
+Epoch:  11%|        | 113/1000 [00:01<00:14, 61.11it/s, loss=1.86]
+Epoch:  11%|        | 113/1000 [00:01<00:14, 61.11it/s, loss=1.85]
+Epoch:  11%|        | 113/1000 [00:01<00:14, 61.11it/s, loss=1.83]
+Epoch:  11%|        | 113/1000 [00:01<00:14, 61.11it/s, loss=1.81]
+Epoch:  11%|        | 113/1000 [00:01<00:14, 61.11it/s, loss=1.8] 
+Epoch:  11%|        | 113/1000 [00:01<00:14, 61.11it/s, loss=1.78]
+Epoch:  12%|        | 120/1000 [00:01<00:14, 61.60it/s, loss=1.78]
+Epoch:  12%|        | 120/1000 [00:01<00:14, 61.60it/s, loss=1.78]
+Epoch:  12%|        | 120/1000 [00:01<00:14, 61.60it/s, loss=1.8] 
+Epoch:  12%|        | 120/1000 [00:01<00:14, 61.60it/s, loss=1.78]
+Epoch:  12%|        | 120/1000 [00:01<00:14, 61.60it/s, loss=1.77]
+Epoch:  12%|        | 120/1000 [00:01<00:14, 61.60it/s, loss=1.78]
+Epoch:  12%|        | 120/1000 [00:02<00:14, 61.60it/s, loss=1.77]
+Epoch:  12%|        | 120/1000 [00:02<00:14, 61.60it/s, loss=1.76]
+Epoch:  13%|        | 127/1000 [00:02<00:14, 60.21it/s, loss=1.76]
+Epoch:  13%|        | 127/1000 [00:02<00:14, 60.21it/s, loss=1.75]
+Epoch:  13%|        | 127/1000 [00:02<00:14, 60.21it/s, loss=1.73]
+Epoch:  13%|        | 127/1000 [00:02<00:14, 60.21it/s, loss=1.73]
+Epoch:  13%|        | 127/1000 [00:02<00:14, 60.21it/s, loss=1.71]
+Epoch:  13%|        | 127/1000 [00:02<00:14, 60.21it/s, loss=1.71]
+Epoch:  13%|        | 127/1000 [00:02<00:14, 60.21it/s, loss=1.7] 
+Epoch:  13%|        | 127/1000 [00:02<00:14, 60.21it/s, loss=1.69]
+Epoch:  13%|        | 134/1000 [00:02<00:14, 61.41it/s, loss=1.69]
+Epoch:  13%|        | 134/1000 [00:02<00:14, 61.41it/s, loss=1.69]
+Epoch:  13%|        | 134/1000 [00:02<00:14, 61.41it/s, loss=1.69]
+Epoch:  13%|        | 134/1000 [00:02<00:14, 61.41it/s, loss=1.68]
+Epoch:  13%|        | 134/1000 [00:02<00:14, 61.41it/s, loss=1.67]
+Epoch:  13%|        | 134/1000 [00:02<00:14, 61.41it/s, loss=1.67]
+Epoch:  13%|        | 134/1000 [00:02<00:14, 61.41it/s, loss=1.67]
+Epoch:  13%|        | 134/1000 [00:02<00:14, 61.41it/s, loss=1.66]
+Epoch:  14%|        | 141/1000 [00:02<00:13, 62.91it/s, loss=1.66]
+Epoch:  14%|        | 141/1000 [00:02<00:13, 62.91it/s, loss=1.65]
+Epoch:  14%|        | 141/1000 [00:02<00:13, 62.91it/s, loss=1.65]
+Epoch:  14%|        | 141/1000 [00:02<00:13, 62.91it/s, loss=1.65]
+Epoch:  14%|        | 141/1000 [00:02<00:13, 62.91it/s, loss=1.64]
+Epoch:  14%|        | 141/1000 [00:02<00:13, 62.91it/s, loss=1.64]
+Epoch:  14%|        | 141/1000 [00:02<00:13, 62.91it/s, loss=1.64]
+Epoch:  14%|        | 141/1000 [00:02<00:13, 62.91it/s, loss=1.64]
+Epoch:  15%|        | 148/1000 [00:02<00:13, 63.33it/s, loss=1.64]
+Epoch:  15%|        | 148/1000 [00:02<00:13, 63.33it/s, loss=1.64]
+Epoch:  15%|        | 148/1000 [00:02<00:13, 63.33it/s, loss=1.63]
+Epoch:  15%|        | 148/1000 [00:02<00:13, 63.33it/s, loss=1.64]
+Epoch:  15%|        | 148/1000 [00:02<00:13, 63.33it/s, loss=1.64]
+Epoch:  15%|        | 148/1000 [00:02<00:13, 63.33it/s, loss=1.64]
+Epoch:  15%|        | 148/1000 [00:02<00:13, 63.33it/s, loss=1.64]
+Epoch:  15%|        | 148/1000 [00:02<00:13, 63.33it/s, loss=1.64]
+Epoch:  16%|        | 155/1000 [00:02<00:13, 64.70it/s, loss=1.64]
+Epoch:  16%|        | 155/1000 [00:02<00:13, 64.70it/s, loss=1.64]
+Epoch:  16%|        | 155/1000 [00:02<00:13, 64.70it/s, loss=1.64]
+Epoch:  16%|        | 155/1000 [00:02<00:13, 64.70it/s, loss=1.63]
+Epoch:  16%|        | 155/1000 [00:02<00:13, 64.70it/s, loss=1.63]
+Epoch:  16%|        | 155/1000 [00:02<00:13, 64.70it/s, loss=1.63]
+Epoch:  16%|        | 155/1000 [00:02<00:13, 64.70it/s, loss=1.63]
+Epoch:  16%|        | 155/1000 [00:02<00:13, 64.70it/s, loss=1.66]
+Epoch:  16%|        | 162/1000 [00:02<00:12, 64.93it/s, loss=1.66]
+Epoch:  16%|        | 162/1000 [00:02<00:12, 64.93it/s, loss=1.65]
+Epoch:  16%|        | 162/1000 [00:02<00:12, 64.93it/s, loss=1.65]
+Epoch:  16%|        | 162/1000 [00:02<00:12, 64.93it/s, loss=1.65]
+Epoch:  16%|        | 162/1000 [00:02<00:12, 64.93it/s, loss=1.65]
+Epoch:  16%|        | 162/1000 [00:02<00:12, 64.93it/s, loss=1.65]
+Epoch:  16%|        | 162/1000 [00:02<00:12, 64.93it/s, loss=1.65]
+Epoch:  16%|        | 162/1000 [00:02<00:12, 64.93it/s, loss=1.64]
+Epoch:  17%|        | 169/1000 [00:02<00:12, 64.33it/s, loss=1.64]
+Epoch:  17%|        | 169/1000 [00:02<00:12, 64.33it/s, loss=1.64]
+Epoch:  17%|        | 169/1000 [00:02<00:12, 64.33it/s, loss=1.64]
+Epoch:  17%|        | 169/1000 [00:02<00:12, 64.33it/s, loss=1.64]
+Epoch:  17%|        | 169/1000 [00:02<00:12, 64.33it/s, loss=1.64]
+Epoch:  17%|        | 169/1000 [00:02<00:12, 64.33it/s, loss=1.64]
+Epoch:  17%|        | 169/1000 [00:02<00:12, 64.33it/s, loss=1.64]
+Epoch:  17%|        | 169/1000 [00:02<00:12, 64.33it/s, loss=1.64]
+Epoch:  18%|        | 176/1000 [00:02<00:12, 64.41it/s, loss=1.64]
+Epoch:  18%|        | 176/1000 [00:02<00:12, 64.41it/s, loss=1.64]
+Epoch:  18%|        | 176/1000 [00:02<00:12, 64.41it/s, loss=1.64]
+Epoch:  18%|        | 176/1000 [00:02<00:12, 64.41it/s, loss=1.64]
+Epoch:  18%|        | 176/1000 [00:02<00:12, 64.41it/s, loss=1.63]
+Epoch:  18%|        | 176/1000 [00:02<00:12, 64.41it/s, loss=1.63]
+Epoch:  18%|        | 176/1000 [00:02<00:12, 64.41it/s, loss=1.63]
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+Epoch:  18%|        | 184/1000 [00:02<00:12, 67.30it/s, loss=1.63]
+Epoch:  18%|        | 184/1000 [00:02<00:12, 67.30it/s, loss=1.62]
+Epoch:  18%|        | 184/1000 [00:02<00:12, 67.30it/s, loss=1.63]
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+Epoch:  18%|        | 184/1000 [00:02<00:12, 67.30it/s, loss=1.62]
+Epoch:  18%|        | 184/1000 [00:02<00:12, 67.30it/s, loss=1.62]
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+Epoch:  19%|        | 191/1000 [00:02<00:12, 67.30it/s, loss=1.62]
+Epoch:  19%|        | 191/1000 [00:03<00:12, 67.30it/s, loss=1.62]
+Epoch:  19%|        | 191/1000 [00:03<00:12, 67.30it/s, loss=1.61]
+Epoch:  19%|        | 191/1000 [00:03<00:12, 67.30it/s, loss=1.61]
+Epoch:  19%|        | 191/1000 [00:03<00:12, 67.30it/s, loss=1.61]
+Epoch:  19%|        | 191/1000 [00:03<00:12, 67.30it/s, loss=1.61]
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+Epoch:  20%|        | 198/1000 [00:03<00:12, 61.76it/s, loss=1.51]
+Epoch:  20%|        | 198/1000 [00:03<00:12, 61.76it/s, loss=1.52]
+Epoch:  20%|        | 198/1000 [00:03<00:12, 61.76it/s, loss=1.49]
+Epoch:  20%|        | 198/1000 [00:03<00:12, 61.76it/s, loss=1.51]
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+Epoch:  20%|        | 205/1000 [00:03<00:12, 62.78it/s, loss=1.53]
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+Epoch:  24%|       | 240/1000 [00:03<00:11, 66.29it/s, loss=1.44]
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+Epoch:  25%|       | 248/1000 [00:03<00:11, 67.88it/s, loss=1.42]
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+Epoch:  26%|       | 263/1000 [00:04<00:11, 66.20it/s, loss=1.42]
+Epoch:  26%|       | 263/1000 [00:04<00:11, 66.20it/s, loss=1.42]
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+Epoch:  28%|       | 277/1000 [00:04<00:11, 65.11it/s, loss=1.42]
+Epoch:  28%|       | 277/1000 [00:04<00:11, 65.11it/s, loss=1.42]
+Epoch:  28%|       | 277/1000 [00:04<00:11, 65.11it/s, loss=1.42]
+Epoch:  28%|       | 277/1000 [00:04<00:11, 65.11it/s, loss=1.42]
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+Epoch:  28%|       | 277/1000 [00:04<00:11, 65.11it/s, loss=1.41]
+Epoch:  28%|       | 277/1000 [00:04<00:11, 65.11it/s, loss=1.41]
+Epoch:  28%|       | 285/1000 [00:04<00:10, 67.00it/s, loss=1.41]
+Epoch:  28%|       | 285/1000 [00:04<00:10, 67.00it/s, loss=1.41]
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+Epoch:  30%|       | 299/1000 [00:04<00:10, 64.66it/s, loss=1.3]
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+Epoch:  37%|      | 371/1000 [00:05<00:09, 66.69it/s, loss=1.21]
+Epoch:  37%|      | 371/1000 [00:05<00:09, 66.69it/s, loss=1.21]
+Epoch:  37%|      | 371/1000 [00:05<00:09, 66.69it/s, loss=1.21]
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+Epoch:  43%|     | 428/1000 [00:06<00:09, 63.36it/s, loss=0.981]
+Epoch:  44%|     | 436/1000 [00:06<00:08, 65.95it/s, loss=0.981]
+Epoch:  44%|     | 436/1000 [00:06<00:08, 65.95it/s, loss=0.978]
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+Epoch:  44%|     | 436/1000 [00:06<00:08, 65.95it/s, loss=0.97] 
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+Epoch:  93%|| 931/1000 [00:14<00:01, 58.66it/s, loss=0.744]
+Epoch:  94%|| 937/1000 [00:14<00:01, 57.95it/s, loss=0.744]
+Epoch:  94%|| 937/1000 [00:14<00:01, 57.95it/s, loss=0.742]
+Epoch:  94%|| 937/1000 [00:15<00:01, 57.95it/s, loss=0.74] 
+Epoch:  94%|| 937/1000 [00:15<00:01, 57.95it/s, loss=0.74]
+Epoch:  94%|| 937/1000 [00:15<00:01, 57.95it/s, loss=0.741]
+Epoch:  94%|| 937/1000 [00:15<00:01, 57.95it/s, loss=0.739]
+Epoch:  94%|| 937/1000 [00:15<00:01, 57.95it/s, loss=0.74] 
+Epoch:  94%|| 937/1000 [00:15<00:01, 57.95it/s, loss=0.74]
+Epoch:  94%|| 944/1000 [00:15<00:00, 59.00it/s, loss=0.74]
+Epoch:  94%|| 944/1000 [00:15<00:00, 59.00it/s, loss=0.738]
+Epoch:  94%|| 944/1000 [00:15<00:00, 59.00it/s, loss=0.742]
+Epoch:  94%|| 944/1000 [00:15<00:00, 59.00it/s, loss=0.739]
+Epoch:  94%|| 944/1000 [00:15<00:00, 59.00it/s, loss=0.737]
+Epoch:  94%|| 944/1000 [00:15<00:00, 59.00it/s, loss=0.737]
+Epoch:  94%|| 944/1000 [00:15<00:00, 59.00it/s, loss=0.734]
+Epoch:  94%|| 944/1000 [00:15<00:00, 59.00it/s, loss=0.732]
+Epoch:  95%|| 951/1000 [00:15<00:00, 60.33it/s, loss=0.732]
+Epoch:  95%|| 951/1000 [00:15<00:00, 60.33it/s, loss=0.735]
+Epoch:  95%|| 951/1000 [00:15<00:00, 60.33it/s, loss=0.734]
+Epoch:  95%|| 951/1000 [00:15<00:00, 60.33it/s, loss=0.738]
+Epoch:  95%|| 951/1000 [00:15<00:00, 60.33it/s, loss=0.736]
+Epoch:  95%|| 951/1000 [00:15<00:00, 60.33it/s, loss=0.736]
+Epoch:  95%|| 951/1000 [00:15<00:00, 60.33it/s, loss=0.737]
+Epoch:  95%|| 951/1000 [00:15<00:00, 60.33it/s, loss=0.737]
+Epoch:  96%|| 958/1000 [00:15<00:00, 60.12it/s, loss=0.737]
+Epoch:  96%|| 958/1000 [00:15<00:00, 60.12it/s, loss=0.741]
+Epoch:  96%|| 958/1000 [00:15<00:00, 60.12it/s, loss=0.739]
+Epoch:  96%|| 958/1000 [00:15<00:00, 60.12it/s, loss=0.738]
+Epoch:  96%|| 958/1000 [00:15<00:00, 60.12it/s, loss=0.737]
+Epoch:  96%|| 958/1000 [00:15<00:00, 60.12it/s, loss=0.738]
+Epoch:  96%|| 958/1000 [00:15<00:00, 60.12it/s, loss=0.738]
+Epoch:  96%|| 958/1000 [00:15<00:00, 60.12it/s, loss=0.743]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.82it/s, loss=0.743]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.82it/s, loss=0.745]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.82it/s, loss=0.744]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.82it/s, loss=0.744]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.82it/s, loss=0.743]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.82it/s, loss=0.742]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.82it/s, loss=0.742]
+Epoch:  97%|| 971/1000 [00:15<00:00, 58.63it/s, loss=0.742]
+Epoch:  97%|| 971/1000 [00:15<00:00, 58.63it/s, loss=0.741]
+Epoch:  97%|| 971/1000 [00:15<00:00, 58.63it/s, loss=0.747]
+Epoch:  97%|| 971/1000 [00:15<00:00, 58.63it/s, loss=0.747]
+Epoch:  97%|| 971/1000 [00:15<00:00, 58.63it/s, loss=0.746]
+Epoch:  97%|| 971/1000 [00:15<00:00, 58.63it/s, loss=0.749]
+Epoch:  97%|| 971/1000 [00:15<00:00, 58.63it/s, loss=0.748]
+Epoch:  97%|| 971/1000 [00:15<00:00, 58.63it/s, loss=0.747]
+Epoch:  98%|| 978/1000 [00:15<00:00, 59.47it/s, loss=0.747]
+Epoch:  98%|| 978/1000 [00:15<00:00, 59.47it/s, loss=0.747]
+Epoch:  98%|| 978/1000 [00:15<00:00, 59.47it/s, loss=0.746]
+Epoch:  98%|| 978/1000 [00:15<00:00, 59.47it/s, loss=0.745]
+Epoch:  98%|| 978/1000 [00:15<00:00, 59.47it/s, loss=0.751]
+Epoch:  98%|| 978/1000 [00:15<00:00, 59.47it/s, loss=0.75] 
+Epoch:  98%|| 978/1000 [00:15<00:00, 59.47it/s, loss=0.748]
+Epoch:  98%|| 978/1000 [00:15<00:00, 59.47it/s, loss=0.749]
+Epoch:  98%|| 985/1000 [00:15<00:00, 60.18it/s, loss=0.749]
+Epoch:  98%|| 985/1000 [00:15<00:00, 60.18it/s, loss=0.748]
+Epoch:  98%|| 985/1000 [00:15<00:00, 60.18it/s, loss=0.748]
+Epoch:  98%|| 985/1000 [00:15<00:00, 60.18it/s, loss=0.749]
+Epoch:  98%|| 985/1000 [00:15<00:00, 60.18it/s, loss=0.748]
+Epoch:  98%|| 985/1000 [00:15<00:00, 60.18it/s, loss=0.747]
+Epoch:  98%|| 985/1000 [00:15<00:00, 60.18it/s, loss=0.747]
+Epoch:  98%|| 985/1000 [00:15<00:00, 60.18it/s, loss=0.747]
+Epoch:  99%|| 992/1000 [00:15<00:00, 61.10it/s, loss=0.747]
+Epoch:  99%|| 992/1000 [00:15<00:00, 61.10it/s, loss=0.746]
+Epoch:  99%|| 992/1000 [00:15<00:00, 61.10it/s, loss=0.745]
+Epoch:  99%|| 992/1000 [00:15<00:00, 61.10it/s, loss=0.747]
+Epoch:  99%|| 992/1000 [00:15<00:00, 61.10it/s, loss=0.748]
+Epoch:  99%|| 992/1000 [00:15<00:00, 61.10it/s, loss=0.747]
+Epoch:  99%|| 992/1000 [00:15<00:00, 61.10it/s, loss=0.747]
+Epoch:  99%|| 992/1000 [00:16<00:00, 61.10it/s, loss=0.746]
+Epoch: 100%|| 999/1000 [00:16<00:00, 60.19it/s, loss=0.746]
+Epoch: 100%|| 999/1000 [00:16<00:00, 60.19it/s, loss=0.745]
+Epoch: 100%|| 1000/1000 [00:16<00:00, 62.40it/s, loss=0.745]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin4] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=2.88]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=6.85]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=5.44]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=4.72]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=4.27]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=3.92]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=3.92]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=3.69]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=3.46]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=3.28]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=3.2] 
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=3.14]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=3.05]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=2.96]
+Epoch:   1%|          | 6/1000 [00:00<00:17, 56.51it/s, loss=2.95]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 65.31it/s, loss=2.95]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 65.31it/s, loss=2.88]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 65.31it/s, loss=2.8] 
+Epoch:   1%|         | 14/1000 [00:00<00:15, 65.31it/s, loss=2.77]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 65.31it/s, loss=2.73]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 65.31it/s, loss=2.72]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 65.31it/s, loss=2.7] 
+Epoch:   1%|         | 14/1000 [00:00<00:15, 65.31it/s, loss=2.65]
+Epoch:   2%|         | 21/1000 [00:00<00:15, 63.18it/s, loss=2.65]
+Epoch:   2%|         | 21/1000 [00:00<00:15, 63.18it/s, loss=2.62]
+Epoch:   2%|         | 21/1000 [00:00<00:15, 63.18it/s, loss=2.62]
+Epoch:   2%|         | 21/1000 [00:00<00:15, 63.18it/s, loss=2.59]
+Epoch:   2%|         | 21/1000 [00:00<00:15, 63.18it/s, loss=2.57]
+Epoch:   2%|         | 21/1000 [00:00<00:15, 63.18it/s, loss=2.57]
+Epoch:   2%|         | 21/1000 [00:00<00:15, 63.18it/s, loss=2.54]
+Epoch:   2%|         | 21/1000 [00:00<00:15, 63.18it/s, loss=2.52]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 64.97it/s, loss=2.52]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 64.97it/s, loss=2.51]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 64.97it/s, loss=2.5] 
+Epoch:   3%|         | 28/1000 [00:00<00:14, 64.97it/s, loss=2.49]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 64.97it/s, loss=2.48]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 64.97it/s, loss=2.47]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 64.97it/s, loss=2.46]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 64.97it/s, loss=2.58]
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.58]
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.57]
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.55]
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.54]
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.54]
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.52]
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.51]
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.5] 
+Epoch:   4%|         | 35/1000 [00:00<00:15, 62.77it/s, loss=2.49]
+Epoch:   4%|         | 43/1000 [00:00<00:14, 65.45it/s, loss=2.49]
+Epoch:   4%|         | 43/1000 [00:00<00:14, 65.45it/s, loss=2.48]
+Epoch:   4%|         | 43/1000 [00:00<00:14, 65.45it/s, loss=2.48]
+Epoch:   4%|         | 43/1000 [00:00<00:14, 65.45it/s, loss=2.46]
+Epoch:   4%|         | 43/1000 [00:00<00:14, 65.45it/s, loss=2.47]
+Epoch:   4%|         | 43/1000 [00:00<00:14, 65.45it/s, loss=2.46]
+Epoch:   4%|         | 43/1000 [00:00<00:14, 65.45it/s, loss=2.45]
+Epoch:   4%|         | 43/1000 [00:00<00:14, 65.45it/s, loss=2.43]
+Epoch:   5%|         | 50/1000 [00:00<00:14, 64.76it/s, loss=2.43]
+Epoch:   5%|         | 50/1000 [00:00<00:14, 64.76it/s, loss=2.42]
+Epoch:   5%|         | 50/1000 [00:00<00:14, 64.76it/s, loss=2.42]
+Epoch:   5%|         | 50/1000 [00:00<00:14, 64.76it/s, loss=2.41]
+Epoch:   5%|         | 50/1000 [00:00<00:14, 64.76it/s, loss=2.4] 
+Epoch:   5%|         | 50/1000 [00:00<00:14, 64.76it/s, loss=2.4]
+Epoch:   5%|         | 50/1000 [00:00<00:14, 64.76it/s, loss=2.39]
+Epoch:   5%|         | 50/1000 [00:00<00:14, 64.76it/s, loss=2.39]
+Epoch:   6%|         | 57/1000 [00:00<00:14, 64.28it/s, loss=2.39]
+Epoch:   6%|         | 57/1000 [00:00<00:14, 64.28it/s, loss=2.41]
+Epoch:   6%|         | 57/1000 [00:00<00:14, 64.28it/s, loss=2.41]
+Epoch:   6%|         | 57/1000 [00:00<00:14, 64.28it/s, loss=2.41]
+Epoch:   6%|         | 57/1000 [00:00<00:14, 64.28it/s, loss=2.4] 
+Epoch:   6%|         | 57/1000 [00:00<00:14, 64.28it/s, loss=2.39]
+Epoch:   6%|         | 57/1000 [00:00<00:14, 64.28it/s, loss=2.4] 
+Epoch:   6%|         | 57/1000 [00:01<00:14, 64.28it/s, loss=2.4]
+Epoch:   6%|         | 64/1000 [00:01<00:14, 63.81it/s, loss=2.4]
+Epoch:   6%|         | 64/1000 [00:01<00:14, 63.81it/s, loss=2.39]
+Epoch:   6%|         | 64/1000 [00:01<00:14, 63.81it/s, loss=2.39]
+Epoch:   6%|         | 64/1000 [00:01<00:14, 63.81it/s, loss=2.4] 
+Epoch:   6%|         | 64/1000 [00:01<00:14, 63.81it/s, loss=2.4]
+Epoch:   6%|         | 64/1000 [00:01<00:14, 63.81it/s, loss=2.39]
+Epoch:   6%|         | 64/1000 [00:01<00:14, 63.81it/s, loss=2.38]
+Epoch:   6%|         | 64/1000 [00:01<00:14, 63.81it/s, loss=2.37]
+Epoch:   7%|         | 71/1000 [00:01<00:14, 65.46it/s, loss=2.37]
+Epoch:   7%|         | 71/1000 [00:01<00:14, 65.46it/s, loss=2.37]
+Epoch:   7%|         | 71/1000 [00:01<00:14, 65.46it/s, loss=2.36]
+Epoch:   7%|         | 71/1000 [00:01<00:14, 65.46it/s, loss=2.36]
+Epoch:   7%|         | 71/1000 [00:01<00:14, 65.46it/s, loss=2.37]
+Epoch:   7%|         | 71/1000 [00:01<00:14, 65.46it/s, loss=2.36]
+Epoch:   7%|         | 71/1000 [00:01<00:14, 65.46it/s, loss=2.36]
+Epoch:   7%|         | 71/1000 [00:01<00:14, 65.46it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 66.15it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 66.15it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 66.15it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 66.15it/s, loss=2.36]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 66.15it/s, loss=2.35]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 66.15it/s, loss=2.35]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 66.15it/s, loss=2.34]
+Epoch:   8%|         | 78/1000 [00:01<00:13, 66.15it/s, loss=2.33]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.33]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.33]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.32]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.32]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.31]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.31]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.31]
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.3] 
+Epoch:   8%|         | 85/1000 [00:01<00:13, 65.76it/s, loss=2.3]
+Epoch:   9%|         | 93/1000 [00:01<00:13, 67.20it/s, loss=2.3]
+Epoch:   9%|         | 93/1000 [00:01<00:13, 67.20it/s, loss=2.3]
+Epoch:   9%|         | 93/1000 [00:01<00:13, 67.20it/s, loss=2.29]
+Epoch:   9%|         | 93/1000 [00:01<00:13, 67.20it/s, loss=2.29]
+Epoch:   9%|         | 93/1000 [00:01<00:13, 67.20it/s, loss=2.29]
+Epoch:   9%|         | 93/1000 [00:01<00:13, 67.20it/s, loss=2.28]
+Epoch:   9%|         | 93/1000 [00:01<00:13, 67.20it/s, loss=2.28]
+Epoch:   9%|         | 93/1000 [00:01<00:13, 67.20it/s, loss=2.28]
+Epoch:  10%|         | 100/1000 [00:01<00:13, 65.70it/s, loss=2.28]
+Epoch:  10%|         | 100/1000 [00:01<00:13, 65.70it/s, loss=1.53]
+Epoch:  10%|         | 100/1000 [00:01<00:13, 65.70it/s, loss=1.87]
+Epoch:  10%|         | 100/1000 [00:01<00:13, 65.70it/s, loss=2.01]
+Epoch:  10%|         | 100/1000 [00:01<00:13, 65.70it/s, loss=2.11]
+Epoch:  10%|         | 100/1000 [00:01<00:13, 65.70it/s, loss=2.1] 
+Epoch:  10%|         | 100/1000 [00:01<00:13, 65.70it/s, loss=2.05]
+Epoch:  10%|         | 100/1000 [00:01<00:13, 65.70it/s, loss=1.98]
+Epoch:  11%|         | 107/1000 [00:01<00:14, 62.41it/s, loss=1.98]
+Epoch:  11%|         | 107/1000 [00:01<00:14, 62.41it/s, loss=1.97]
+Epoch:  11%|         | 107/1000 [00:01<00:14, 62.41it/s, loss=1.93]
+Epoch:  11%|         | 107/1000 [00:01<00:14, 62.41it/s, loss=1.89]
+Epoch:  11%|         | 107/1000 [00:01<00:14, 62.41it/s, loss=1.89]
+Epoch:  11%|         | 107/1000 [00:01<00:14, 62.41it/s, loss=1.92]
+Epoch:  11%|         | 107/1000 [00:01<00:14, 62.41it/s, loss=1.9] 
+Epoch:  11%|         | 107/1000 [00:01<00:14, 62.41it/s, loss=1.88]
+Epoch:  11%|        | 114/1000 [00:01<00:14, 62.08it/s, loss=1.88]
+Epoch:  11%|        | 114/1000 [00:01<00:14, 62.08it/s, loss=1.86]
+Epoch:  11%|        | 114/1000 [00:01<00:14, 62.08it/s, loss=1.85]
+Epoch:  11%|        | 114/1000 [00:01<00:14, 62.08it/s, loss=1.83]
+Epoch:  11%|        | 114/1000 [00:01<00:14, 62.08it/s, loss=1.81]
+Epoch:  11%|        | 114/1000 [00:01<00:14, 62.08it/s, loss=1.8] 
+Epoch:  11%|        | 114/1000 [00:01<00:14, 62.08it/s, loss=1.78]
+Epoch:  11%|        | 114/1000 [00:01<00:14, 62.08it/s, loss=1.78]
+Epoch:  12%|        | 121/1000 [00:01<00:13, 62.88it/s, loss=1.78]
+Epoch:  12%|        | 121/1000 [00:01<00:13, 62.88it/s, loss=1.8] 
+Epoch:  12%|        | 121/1000 [00:01<00:13, 62.88it/s, loss=1.78]
+Epoch:  12%|        | 121/1000 [00:01<00:13, 62.88it/s, loss=1.77]
+Epoch:  12%|        | 121/1000 [00:01<00:13, 62.88it/s, loss=1.78]
+Epoch:  12%|        | 121/1000 [00:01<00:13, 62.88it/s, loss=1.77]
+Epoch:  12%|        | 121/1000 [00:01<00:13, 62.88it/s, loss=1.76]
+Epoch:  12%|        | 121/1000 [00:01<00:13, 62.88it/s, loss=1.75]
+Epoch:  13%|        | 128/1000 [00:01<00:13, 63.36it/s, loss=1.75]
+Epoch:  13%|        | 128/1000 [00:02<00:13, 63.36it/s, loss=1.73]
+Epoch:  13%|        | 128/1000 [00:02<00:13, 63.36it/s, loss=1.73]
+Epoch:  13%|        | 128/1000 [00:02<00:13, 63.36it/s, loss=1.71]
+Epoch:  13%|        | 128/1000 [00:02<00:13, 63.36it/s, loss=1.71]
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+Epoch:  17%|        | 172/1000 [00:02<00:12, 66.28it/s, loss=1.64]
+Epoch:  17%|        | 172/1000 [00:02<00:12, 66.28it/s, loss=1.64]
+Epoch:  17%|        | 172/1000 [00:02<00:12, 66.28it/s, loss=1.64]
+Epoch:  17%|        | 172/1000 [00:02<00:12, 66.28it/s, loss=1.64]
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+Epoch:  18%|        | 180/1000 [00:02<00:12, 67.71it/s, loss=1.63]
+Epoch:  18%|        | 180/1000 [00:02<00:12, 67.71it/s, loss=1.62]
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+Epoch:  19%|        | 187/1000 [00:02<00:12, 67.71it/s, loss=1.62]
+Epoch:  19%|        | 187/1000 [00:02<00:12, 67.71it/s, loss=1.62]
+Epoch:  19%|        | 187/1000 [00:02<00:12, 67.71it/s, loss=1.62]
+Epoch:  19%|        | 187/1000 [00:02<00:12, 67.71it/s, loss=1.62]
+Epoch:  19%|        | 187/1000 [00:02<00:12, 67.71it/s, loss=1.62]
+Epoch:  19%|        | 187/1000 [00:02<00:12, 67.71it/s, loss=1.62]
+Epoch:  19%|        | 187/1000 [00:02<00:12, 67.71it/s, loss=1.61]
+Epoch:  19%|        | 187/1000 [00:02<00:12, 67.71it/s, loss=1.61]
+Epoch:  19%|        | 194/1000 [00:02<00:11, 67.17it/s, loss=1.61]
+Epoch:  19%|        | 194/1000 [00:03<00:11, 67.17it/s, loss=1.61]
+Epoch:  19%|        | 194/1000 [00:03<00:11, 67.17it/s, loss=1.61]
+Epoch:  19%|        | 194/1000 [00:03<00:11, 67.17it/s, loss=1.6] 
+Epoch:  19%|        | 194/1000 [00:03<00:11, 67.17it/s, loss=1.6]
+Epoch:  19%|        | 194/1000 [00:03<00:11, 67.17it/s, loss=1.61]
+Epoch:  19%|        | 194/1000 [00:03<00:11, 67.17it/s, loss=1.61]
+Epoch:  19%|        | 194/1000 [00:03<00:11, 67.17it/s, loss=1.48]
+Epoch:  19%|        | 194/1000 [00:03<00:11, 67.17it/s, loss=1.51]
+Epoch:  20%|        | 202/1000 [00:03<00:11, 67.57it/s, loss=1.51]
+Epoch:  20%|        | 202/1000 [00:03<00:11, 67.57it/s, loss=1.52]
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+Epoch:  20%|        | 202/1000 [00:03<00:11, 67.57it/s, loss=1.51]
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+Epoch:  20%|        | 202/1000 [00:03<00:11, 67.57it/s, loss=1.54]
+Epoch:  21%|        | 209/1000 [00:03<00:11, 66.53it/s, loss=1.54]
+Epoch:  21%|        | 209/1000 [00:03<00:11, 66.53it/s, loss=1.53]
+Epoch:  21%|        | 209/1000 [00:03<00:11, 66.53it/s, loss=1.53]
+Epoch:  21%|        | 209/1000 [00:03<00:11, 66.53it/s, loss=1.52]
+Epoch:  21%|        | 209/1000 [00:03<00:11, 66.53it/s, loss=1.51]
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+Epoch:  23%|       | 230/1000 [00:03<00:11, 65.63it/s, loss=1.45]
+Epoch:  23%|       | 230/1000 [00:03<00:11, 65.63it/s, loss=1.45]
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+Epoch:  24%|       | 237/1000 [00:03<00:11, 65.89it/s, loss=1.44]
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+Epoch:  27%|       | 274/1000 [00:04<00:10, 66.90it/s, loss=1.42]
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+Epoch:  28%|       | 281/1000 [00:04<00:10, 67.68it/s, loss=1.41]
+Epoch:  28%|       | 281/1000 [00:04<00:10, 67.68it/s, loss=1.41]
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+Epoch:  28%|       | 281/1000 [00:04<00:10, 67.68it/s, loss=1.41]
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+Epoch:  31%|       | 309/1000 [00:04<00:11, 61.58it/s, loss=1.3]
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+Epoch:  31%|       | 309/1000 [00:04<00:11, 61.58it/s, loss=1.29]
+Epoch:  31%|       | 309/1000 [00:04<00:11, 61.58it/s, loss=1.29]
+Epoch:  31%|       | 309/1000 [00:04<00:11, 61.58it/s, loss=1.31]
+Epoch:  31%|       | 309/1000 [00:04<00:11, 61.58it/s, loss=1.31]
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+Epoch:  32%|      | 316/1000 [00:04<00:11, 59.42it/s, loss=1.32]
+Epoch:  32%|      | 316/1000 [00:04<00:11, 59.42it/s, loss=1.32]
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+Epoch:  32%|      | 316/1000 [00:04<00:11, 59.42it/s, loss=1.31]
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+Epoch:  51%|     | 509/1000 [00:07<00:07, 67.22it/s, loss=0.927]
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+Epoch:  52%|    | 523/1000 [00:08<00:07, 65.73it/s, loss=0.904]
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+Epoch:  57%|    | 574/1000 [00:08<00:06, 68.04it/s, loss=0.866]
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+Epoch:  89%| | 886/1000 [00:13<00:01, 60.21it/s, loss=0.805]
+Epoch:  89%| | 886/1000 [00:13<00:01, 60.21it/s, loss=0.804]
+Epoch:  89%| | 886/1000 [00:13<00:01, 60.21it/s, loss=0.802]
+Epoch:  89%| | 886/1000 [00:14<00:01, 60.21it/s, loss=0.802]
+Epoch:  89%| | 886/1000 [00:14<00:01, 60.21it/s, loss=0.802]
+Epoch:  89%| | 886/1000 [00:14<00:01, 60.21it/s, loss=0.803]
+Epoch:  89%| | 886/1000 [00:14<00:01, 60.21it/s, loss=0.802]
+Epoch:  89%| | 886/1000 [00:14<00:01, 60.21it/s, loss=0.802]
+Epoch:  89%| | 893/1000 [00:14<00:01, 60.36it/s, loss=0.802]
+Epoch:  89%| | 893/1000 [00:14<00:01, 60.36it/s, loss=0.8]  
+Epoch:  89%| | 893/1000 [00:14<00:01, 60.36it/s, loss=0.8]
+Epoch:  89%| | 893/1000 [00:14<00:01, 60.36it/s, loss=0.8]
+Epoch:  89%| | 893/1000 [00:14<00:01, 60.36it/s, loss=0.799]
+Epoch:  89%| | 893/1000 [00:14<00:01, 60.36it/s, loss=0.798]
+Epoch:  89%| | 893/1000 [00:14<00:01, 60.36it/s, loss=0.797]
+Epoch:  89%| | 893/1000 [00:14<00:01, 60.36it/s, loss=0.799]
+Epoch:  90%| | 900/1000 [00:14<00:01, 59.15it/s, loss=0.799]
+Epoch:  90%| | 900/1000 [00:14<00:01, 59.15it/s, loss=0.741]
+Epoch:  90%| | 900/1000 [00:14<00:01, 59.15it/s, loss=0.681]
+Epoch:  90%| | 900/1000 [00:14<00:01, 59.15it/s, loss=0.658]
+Epoch:  90%| | 900/1000 [00:14<00:01, 59.15it/s, loss=0.704]
+Epoch:  90%| | 900/1000 [00:14<00:01, 59.15it/s, loss=0.756]
+Epoch:  90%| | 900/1000 [00:14<00:01, 59.15it/s, loss=0.755]
+Epoch:  90%| | 900/1000 [00:14<00:01, 59.15it/s, loss=0.756]
+Epoch:  91%| | 907/1000 [00:14<00:01, 59.17it/s, loss=0.756]
+Epoch:  91%| | 907/1000 [00:14<00:01, 59.17it/s, loss=0.771]
+Epoch:  91%| | 907/1000 [00:14<00:01, 59.17it/s, loss=0.762]
+Epoch:  91%| | 907/1000 [00:14<00:01, 59.17it/s, loss=0.752]
+Epoch:  91%| | 907/1000 [00:14<00:01, 59.17it/s, loss=0.745]
+Epoch:  91%| | 907/1000 [00:14<00:01, 59.17it/s, loss=0.73] 
+Epoch:  91%| | 907/1000 [00:14<00:01, 59.17it/s, loss=0.739]
+Epoch:  91%|| 913/1000 [00:14<00:01, 55.42it/s, loss=0.739]
+Epoch:  91%|| 913/1000 [00:14<00:01, 55.42it/s, loss=0.743]
+Epoch:  91%|| 913/1000 [00:14<00:01, 55.42it/s, loss=0.747]
+Epoch:  91%|| 913/1000 [00:14<00:01, 55.42it/s, loss=0.74] 
+Epoch:  91%|| 913/1000 [00:14<00:01, 55.42it/s, loss=0.737]
+Epoch:  91%|| 913/1000 [00:14<00:01, 55.42it/s, loss=0.729]
+Epoch:  91%|| 913/1000 [00:14<00:01, 55.42it/s, loss=0.723]
+Epoch:  91%|| 913/1000 [00:14<00:01, 55.42it/s, loss=0.731]
+Epoch:  92%|| 920/1000 [00:14<00:01, 57.40it/s, loss=0.731]
+Epoch:  92%|| 920/1000 [00:14<00:01, 57.40it/s, loss=0.734]
+Epoch:  92%|| 920/1000 [00:14<00:01, 57.40it/s, loss=0.738]
+Epoch:  92%|| 920/1000 [00:14<00:01, 57.40it/s, loss=0.736]
+Epoch:  92%|| 920/1000 [00:14<00:01, 57.40it/s, loss=0.736]
+Epoch:  92%|| 920/1000 [00:14<00:01, 57.40it/s, loss=0.732]
+Epoch:  92%|| 920/1000 [00:14<00:01, 57.40it/s, loss=0.728]
+Epoch:  92%|| 920/1000 [00:14<00:01, 57.40it/s, loss=0.734]
+Epoch:  93%|| 927/1000 [00:14<00:01, 57.72it/s, loss=0.734]
+Epoch:  93%|| 927/1000 [00:14<00:01, 57.72it/s, loss=0.735]
+Epoch:  93%|| 927/1000 [00:14<00:01, 57.72it/s, loss=0.733]
+Epoch:  93%|| 927/1000 [00:14<00:01, 57.72it/s, loss=0.734]
+Epoch:  93%|| 927/1000 [00:14<00:01, 57.72it/s, loss=0.741]
+Epoch:  93%|| 927/1000 [00:14<00:01, 57.72it/s, loss=0.742]
+Epoch:  93%|| 927/1000 [00:14<00:01, 57.72it/s, loss=0.744]
+Epoch:  93%|| 933/1000 [00:14<00:01, 57.05it/s, loss=0.744]
+Epoch:  93%|| 933/1000 [00:14<00:01, 57.05it/s, loss=0.745]
+Epoch:  93%|| 933/1000 [00:14<00:01, 57.05it/s, loss=0.741]
+Epoch:  93%|| 933/1000 [00:14<00:01, 57.05it/s, loss=0.741]
+Epoch:  93%|| 933/1000 [00:14<00:01, 57.05it/s, loss=0.744]
+Epoch:  93%|| 933/1000 [00:14<00:01, 57.05it/s, loss=0.742]
+Epoch:  93%|| 933/1000 [00:14<00:01, 57.05it/s, loss=0.74] 
+Epoch:  93%|| 933/1000 [00:14<00:01, 57.05it/s, loss=0.74]
+Epoch:  94%|| 940/1000 [00:14<00:01, 59.81it/s, loss=0.74]
+Epoch:  94%|| 940/1000 [00:14<00:01, 59.81it/s, loss=0.741]
+Epoch:  94%|| 940/1000 [00:14<00:01, 59.81it/s, loss=0.739]
+Epoch:  94%|| 940/1000 [00:14<00:01, 59.81it/s, loss=0.74] 
+Epoch:  94%|| 940/1000 [00:14<00:01, 59.81it/s, loss=0.74]
+Epoch:  94%|| 940/1000 [00:14<00:01, 59.81it/s, loss=0.738]
+Epoch:  94%|| 940/1000 [00:14<00:01, 59.81it/s, loss=0.742]
+Epoch:  94%|| 940/1000 [00:14<00:01, 59.81it/s, loss=0.739]
+Epoch:  95%|| 947/1000 [00:14<00:00, 60.54it/s, loss=0.739]
+Epoch:  95%|| 947/1000 [00:15<00:00, 60.54it/s, loss=0.737]
+Epoch:  95%|| 947/1000 [00:15<00:00, 60.54it/s, loss=0.737]
+Epoch:  95%|| 947/1000 [00:15<00:00, 60.54it/s, loss=0.734]
+Epoch:  95%|| 947/1000 [00:15<00:00, 60.54it/s, loss=0.732]
+Epoch:  95%|| 947/1000 [00:15<00:00, 60.54it/s, loss=0.735]
+Epoch:  95%|| 947/1000 [00:15<00:00, 60.54it/s, loss=0.734]
+Epoch:  95%|| 947/1000 [00:15<00:00, 60.54it/s, loss=0.738]
+Epoch:  95%|| 954/1000 [00:15<00:00, 57.58it/s, loss=0.738]
+Epoch:  95%|| 954/1000 [00:15<00:00, 57.58it/s, loss=0.736]
+Epoch:  95%|| 954/1000 [00:15<00:00, 57.58it/s, loss=0.736]
+Epoch:  95%|| 954/1000 [00:15<00:00, 57.58it/s, loss=0.737]
+Epoch:  95%|| 954/1000 [00:15<00:00, 57.58it/s, loss=0.737]
+Epoch:  95%|| 954/1000 [00:15<00:00, 57.58it/s, loss=0.741]
+Epoch:  95%|| 954/1000 [00:15<00:00, 57.58it/s, loss=0.739]
+Epoch:  95%|| 954/1000 [00:15<00:00, 57.58it/s, loss=0.738]
+Epoch:  96%|| 961/1000 [00:15<00:00, 59.16it/s, loss=0.738]
+Epoch:  96%|| 961/1000 [00:15<00:00, 59.16it/s, loss=0.737]
+Epoch:  96%|| 961/1000 [00:15<00:00, 59.16it/s, loss=0.738]
+Epoch:  96%|| 961/1000 [00:15<00:00, 59.16it/s, loss=0.738]
+Epoch:  96%|| 961/1000 [00:15<00:00, 59.16it/s, loss=0.743]
+Epoch:  96%|| 961/1000 [00:15<00:00, 59.16it/s, loss=0.745]
+Epoch:  96%|| 961/1000 [00:15<00:00, 59.16it/s, loss=0.744]
+Epoch:  96%|| 961/1000 [00:15<00:00, 59.16it/s, loss=0.744]
+Epoch:  97%|| 968/1000 [00:15<00:00, 60.75it/s, loss=0.744]
+Epoch:  97%|| 968/1000 [00:15<00:00, 60.75it/s, loss=0.743]
+Epoch:  97%|| 968/1000 [00:15<00:00, 60.75it/s, loss=0.742]
+Epoch:  97%|| 968/1000 [00:15<00:00, 60.75it/s, loss=0.742]
+Epoch:  97%|| 968/1000 [00:15<00:00, 60.75it/s, loss=0.741]
+Epoch:  97%|| 968/1000 [00:15<00:00, 60.75it/s, loss=0.747]
+Epoch:  97%|| 968/1000 [00:15<00:00, 60.75it/s, loss=0.747]
+Epoch:  97%|| 968/1000 [00:15<00:00, 60.75it/s, loss=0.746]
+Epoch:  98%|| 975/1000 [00:15<00:00, 60.80it/s, loss=0.746]
+Epoch:  98%|| 975/1000 [00:15<00:00, 60.80it/s, loss=0.749]
+Epoch:  98%|| 975/1000 [00:15<00:00, 60.80it/s, loss=0.748]
+Epoch:  98%|| 975/1000 [00:15<00:00, 60.80it/s, loss=0.747]
+Epoch:  98%|| 975/1000 [00:15<00:00, 60.80it/s, loss=0.747]
+Epoch:  98%|| 975/1000 [00:15<00:00, 60.80it/s, loss=0.746]
+Epoch:  98%|| 975/1000 [00:15<00:00, 60.80it/s, loss=0.745]
+Epoch:  98%|| 975/1000 [00:15<00:00, 60.80it/s, loss=0.751]
+Epoch:  98%|| 982/1000 [00:15<00:00, 59.35it/s, loss=0.751]
+Epoch:  98%|| 982/1000 [00:15<00:00, 59.35it/s, loss=0.75] 
+Epoch:  98%|| 982/1000 [00:15<00:00, 59.35it/s, loss=0.748]
+Epoch:  98%|| 982/1000 [00:15<00:00, 59.35it/s, loss=0.749]
+Epoch:  98%|| 982/1000 [00:15<00:00, 59.35it/s, loss=0.748]
+Epoch:  98%|| 982/1000 [00:15<00:00, 59.35it/s, loss=0.748]
+Epoch:  98%|| 982/1000 [00:15<00:00, 59.35it/s, loss=0.749]
+Epoch:  99%|| 988/1000 [00:15<00:00, 59.00it/s, loss=0.749]
+Epoch:  99%|| 988/1000 [00:15<00:00, 59.00it/s, loss=0.748]
+Epoch:  99%|| 988/1000 [00:15<00:00, 59.00it/s, loss=0.747]
+Epoch:  99%|| 988/1000 [00:15<00:00, 59.00it/s, loss=0.747]
+Epoch:  99%|| 988/1000 [00:15<00:00, 59.00it/s, loss=0.747]
+Epoch:  99%|| 988/1000 [00:15<00:00, 59.00it/s, loss=0.746]
+Epoch:  99%|| 988/1000 [00:15<00:00, 59.00it/s, loss=0.745]
+Epoch:  99%|| 988/1000 [00:15<00:00, 59.00it/s, loss=0.747]
+Epoch: 100%|| 995/1000 [00:15<00:00, 59.11it/s, loss=0.747]
+Epoch: 100%|| 995/1000 [00:15<00:00, 59.11it/s, loss=0.748]
+Epoch: 100%|| 995/1000 [00:15<00:00, 59.11it/s, loss=0.747]
+Epoch: 100%|| 995/1000 [00:15<00:00, 59.11it/s, loss=0.747]
+Epoch: 100%|| 995/1000 [00:15<00:00, 59.11it/s, loss=0.746]
+Epoch: 100%|| 995/1000 [00:15<00:00, 59.11it/s, loss=0.745]
+Epoch: 100%|| 1000/1000 [00:15<00:00, 62.88it/s, loss=0.745]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin5] 
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=2.88]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=6.85]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=5.44]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=4.72]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=4.27]
+Epoch:   0%|          | 0/1000 [00:00<?, ?it/s, loss=3.92]
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=3.92]
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=3.69]
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=3.46]
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=3.28]
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=3.2] 
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=3.14]
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=3.05]
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=2.96]
+Epoch:   1%|          | 6/1000 [00:00<00:18, 53.63it/s, loss=2.95]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 64.09it/s, loss=2.95]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 64.09it/s, loss=2.88]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 64.09it/s, loss=2.8] 
+Epoch:   1%|         | 14/1000 [00:00<00:15, 64.09it/s, loss=2.77]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 64.09it/s, loss=2.73]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 64.09it/s, loss=2.72]
+Epoch:   1%|         | 14/1000 [00:00<00:15, 64.09it/s, loss=2.7] 
+Epoch:   1%|         | 14/1000 [00:00<00:15, 64.09it/s, loss=2.65]
+Epoch:   2%|         | 21/1000 [00:00<00:14, 66.47it/s, loss=2.65]
+Epoch:   2%|         | 21/1000 [00:00<00:14, 66.47it/s, loss=2.62]
+Epoch:   2%|         | 21/1000 [00:00<00:14, 66.47it/s, loss=2.62]
+Epoch:   2%|         | 21/1000 [00:00<00:14, 66.47it/s, loss=2.59]
+Epoch:   2%|         | 21/1000 [00:00<00:14, 66.47it/s, loss=2.57]
+Epoch:   2%|         | 21/1000 [00:00<00:14, 66.47it/s, loss=2.57]
+Epoch:   2%|         | 21/1000 [00:00<00:14, 66.47it/s, loss=2.54]
+Epoch:   2%|         | 21/1000 [00:00<00:14, 66.47it/s, loss=2.52]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 66.82it/s, loss=2.52]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 66.82it/s, loss=2.51]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 66.82it/s, loss=2.5] 
+Epoch:   3%|         | 28/1000 [00:00<00:14, 66.82it/s, loss=2.49]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 66.82it/s, loss=2.48]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 66.82it/s, loss=2.47]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 66.82it/s, loss=2.46]
+Epoch:   3%|         | 28/1000 [00:00<00:14, 66.82it/s, loss=2.58]
+Epoch:   4%|         | 35/1000 [00:00<00:14, 66.62it/s, loss=2.58]
+Epoch:   4%|         | 35/1000 [00:00<00:14, 66.62it/s, loss=2.57]
+Epoch:   4%|         | 35/1000 [00:00<00:14, 66.62it/s, loss=2.55]
+Epoch:   4%|         | 35/1000 [00:00<00:14, 66.62it/s, loss=2.54]
+Epoch:   4%|         | 35/1000 [00:00<00:14, 66.62it/s, loss=2.54]
+Epoch:   4%|         | 35/1000 [00:00<00:14, 66.62it/s, loss=2.52]
+Epoch:   4%|         | 35/1000 [00:00<00:14, 66.62it/s, loss=2.51]
+Epoch:   4%|         | 35/1000 [00:00<00:14, 66.62it/s, loss=2.5] 
+Epoch:   4%|         | 42/1000 [00:00<00:14, 67.55it/s, loss=2.5]
+Epoch:   4%|         | 42/1000 [00:00<00:14, 67.55it/s, loss=2.49]
+Epoch:   4%|         | 42/1000 [00:00<00:14, 67.55it/s, loss=2.48]
+Epoch:   4%|         | 42/1000 [00:00<00:14, 67.55it/s, loss=2.48]
+Epoch:   4%|         | 42/1000 [00:00<00:14, 67.55it/s, loss=2.46]
+Epoch:   4%|         | 42/1000 [00:00<00:14, 67.55it/s, loss=2.47]
+Epoch:   4%|         | 42/1000 [00:00<00:14, 67.55it/s, loss=2.46]
+Epoch:   4%|         | 42/1000 [00:00<00:14, 67.55it/s, loss=2.45]
+Epoch:   5%|         | 49/1000 [00:00<00:14, 64.97it/s, loss=2.45]
+Epoch:   5%|         | 49/1000 [00:00<00:14, 64.97it/s, loss=2.43]
+Epoch:   5%|         | 49/1000 [00:00<00:14, 64.97it/s, loss=2.42]
+Epoch:   5%|         | 49/1000 [00:00<00:14, 64.97it/s, loss=2.42]
+Epoch:   5%|         | 49/1000 [00:00<00:14, 64.97it/s, loss=2.41]
+Epoch:   5%|         | 49/1000 [00:00<00:14, 64.97it/s, loss=2.4] 
+Epoch:   5%|         | 49/1000 [00:00<00:14, 64.97it/s, loss=2.4]
+Epoch:   5%|         | 49/1000 [00:00<00:14, 64.97it/s, loss=2.39]
+Epoch:   6%|         | 56/1000 [00:00<00:14, 65.09it/s, loss=2.39]
+Epoch:   6%|         | 56/1000 [00:00<00:14, 65.09it/s, loss=2.39]
+Epoch:   6%|         | 56/1000 [00:00<00:14, 65.09it/s, loss=2.41]
+Epoch:   6%|         | 56/1000 [00:00<00:14, 65.09it/s, loss=2.41]
+Epoch:   6%|         | 56/1000 [00:00<00:14, 65.09it/s, loss=2.41]
+Epoch:   6%|         | 56/1000 [00:00<00:14, 65.09it/s, loss=2.4] 
+Epoch:   6%|         | 56/1000 [00:00<00:14, 65.09it/s, loss=2.39]
+Epoch:   6%|         | 56/1000 [00:00<00:14, 65.09it/s, loss=2.4] 
+Epoch:   6%|         | 63/1000 [00:00<00:14, 63.49it/s, loss=2.4]
+Epoch:   6%|         | 63/1000 [00:01<00:14, 63.49it/s, loss=2.4]
+Epoch:   6%|         | 63/1000 [00:01<00:14, 63.49it/s, loss=2.39]
+Epoch:   6%|         | 63/1000 [00:01<00:14, 63.49it/s, loss=2.39]
+Epoch:   6%|         | 63/1000 [00:01<00:14, 63.49it/s, loss=2.4] 
+Epoch:   6%|         | 63/1000 [00:01<00:14, 63.49it/s, loss=2.4]
+Epoch:   6%|         | 63/1000 [00:01<00:14, 63.49it/s, loss=2.39]
+Epoch:   6%|         | 63/1000 [00:01<00:14, 63.49it/s, loss=2.38]
+Epoch:   7%|         | 70/1000 [00:01<00:15, 59.61it/s, loss=2.38]
+Epoch:   7%|         | 70/1000 [00:01<00:15, 59.61it/s, loss=2.37]
+Epoch:   7%|         | 70/1000 [00:01<00:15, 59.61it/s, loss=2.37]
+Epoch:   7%|         | 70/1000 [00:01<00:15, 59.61it/s, loss=2.36]
+Epoch:   7%|         | 70/1000 [00:01<00:15, 59.61it/s, loss=2.36]
+Epoch:   7%|         | 70/1000 [00:01<00:15, 59.61it/s, loss=2.37]
+Epoch:   7%|         | 70/1000 [00:01<00:15, 59.61it/s, loss=2.36]
+Epoch:   7%|         | 70/1000 [00:01<00:15, 59.61it/s, loss=2.36]
+Epoch:   8%|         | 77/1000 [00:01<00:15, 60.72it/s, loss=2.36]
+Epoch:   8%|         | 77/1000 [00:01<00:15, 60.72it/s, loss=2.36]
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+Epoch:   8%|         | 77/1000 [00:01<00:15, 60.72it/s, loss=2.35]
+Epoch:   8%|         | 77/1000 [00:01<00:15, 60.72it/s, loss=2.34]
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+Epoch:   8%|         | 84/1000 [00:01<00:15, 60.73it/s, loss=2.32]
+Epoch:   8%|         | 84/1000 [00:01<00:15, 60.73it/s, loss=2.31]
+Epoch:   8%|         | 84/1000 [00:01<00:15, 60.73it/s, loss=2.31]
+Epoch:   8%|         | 84/1000 [00:01<00:15, 60.73it/s, loss=2.31]
+Epoch:   9%|         | 91/1000 [00:01<00:14, 60.72it/s, loss=2.31]
+Epoch:   9%|         | 91/1000 [00:01<00:14, 60.72it/s, loss=2.3] 
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+Epoch:   9%|         | 91/1000 [00:01<00:14, 60.72it/s, loss=2.29]
+Epoch:   9%|         | 91/1000 [00:01<00:14, 60.72it/s, loss=2.28]
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+Epoch:  10%|         | 98/1000 [00:01<00:14, 61.66it/s, loss=2.28]
+Epoch:  10%|         | 98/1000 [00:01<00:14, 61.66it/s, loss=2.28]
+Epoch:  10%|         | 98/1000 [00:01<00:14, 61.66it/s, loss=1.53]
+Epoch:  10%|         | 98/1000 [00:01<00:14, 61.66it/s, loss=1.87]
+Epoch:  10%|         | 98/1000 [00:01<00:14, 61.66it/s, loss=2.01]
+Epoch:  10%|         | 98/1000 [00:01<00:14, 61.66it/s, loss=2.11]
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+Epoch:  10%|         | 105/1000 [00:01<00:14, 63.24it/s, loss=1.89]
+Epoch:  10%|         | 105/1000 [00:01<00:14, 63.24it/s, loss=1.89]
+Epoch:  10%|         | 105/1000 [00:01<00:14, 63.24it/s, loss=1.92]
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+Epoch:  11%|         | 112/1000 [00:01<00:14, 63.10it/s, loss=1.9] 
+Epoch:  11%|         | 112/1000 [00:01<00:14, 63.10it/s, loss=1.88]
+Epoch:  11%|         | 112/1000 [00:01<00:14, 63.10it/s, loss=1.86]
+Epoch:  11%|         | 112/1000 [00:01<00:14, 63.10it/s, loss=1.85]
+Epoch:  11%|         | 112/1000 [00:01<00:14, 63.10it/s, loss=1.83]
+Epoch:  11%|         | 112/1000 [00:01<00:14, 63.10it/s, loss=1.81]
+Epoch:  11%|         | 112/1000 [00:01<00:14, 63.10it/s, loss=1.8] 
+Epoch:  11%|         | 112/1000 [00:01<00:14, 63.10it/s, loss=1.78]
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+Epoch:  13%|        | 127/1000 [00:01<00:12, 67.27it/s, loss=1.76]
+Epoch:  13%|        | 127/1000 [00:01<00:12, 67.27it/s, loss=1.75]
+Epoch:  13%|        | 127/1000 [00:02<00:12, 67.27it/s, loss=1.73]
+Epoch:  13%|        | 127/1000 [00:02<00:12, 67.27it/s, loss=1.73]
+Epoch:  13%|        | 127/1000 [00:02<00:12, 67.27it/s, loss=1.71]
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+Epoch:  13%|        | 134/1000 [00:02<00:13, 66.50it/s, loss=1.67]
+Epoch:  13%|        | 134/1000 [00:02<00:13, 66.50it/s, loss=1.67]
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+Epoch:  28%|       | 277/1000 [00:04<00:10, 67.74it/s, loss=1.42]
+Epoch:  28%|       | 277/1000 [00:04<00:10, 67.74it/s, loss=1.42]
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+Epoch:  28%|       | 277/1000 [00:04<00:10, 67.74it/s, loss=1.41]
+Epoch:  28%|       | 277/1000 [00:04<00:10, 67.74it/s, loss=1.41]
+Epoch:  28%|       | 284/1000 [00:04<00:11, 63.43it/s, loss=1.41]
+Epoch:  28%|       | 284/1000 [00:04<00:11, 63.43it/s, loss=1.41]
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+Epoch:  50%|     | 499/1000 [00:07<00:07, 64.75it/s, loss=0.863]
+Epoch:  51%|     | 506/1000 [00:07<00:07, 62.80it/s, loss=0.863]
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+Epoch:  51%|    | 514/1000 [00:08<00:07, 64.34it/s, loss=0.942]
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+Epoch:  52%|    | 521/1000 [00:08<00:07, 64.69it/s, loss=0.902]
+Epoch:  52%|    | 521/1000 [00:08<00:07, 64.69it/s, loss=0.904]
+Epoch:  52%|    | 521/1000 [00:08<00:07, 64.69it/s, loss=0.913]
+Epoch:  52%|    | 521/1000 [00:08<00:07, 64.69it/s, loss=0.916]
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+Epoch:  52%|    | 521/1000 [00:08<00:07, 64.69it/s, loss=0.911]
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+Epoch:  53%|    | 528/1000 [00:08<00:07, 64.73it/s, loss=0.911]
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+Epoch:  53%|    | 528/1000 [00:08<00:07, 64.73it/s, loss=0.911]
+Epoch:  53%|    | 528/1000 [00:08<00:07, 64.73it/s, loss=0.909]
+Epoch:  53%|    | 528/1000 [00:08<00:07, 64.73it/s, loss=0.907]
+Epoch:  53%|    | 528/1000 [00:08<00:07, 64.73it/s, loss=0.906]
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+Epoch:  84%| | 835/1000 [00:13<00:02, 58.27it/s, loss=0.828]
+Epoch:  84%| | 835/1000 [00:13<00:02, 58.27it/s, loss=0.826]
+Epoch:  84%| | 835/1000 [00:13<00:02, 58.27it/s, loss=0.825]
+Epoch:  84%| | 835/1000 [00:13<00:02, 58.27it/s, loss=0.822]
+Epoch:  84%| | 835/1000 [00:13<00:02, 58.27it/s, loss=0.822]
+Epoch:  84%| | 835/1000 [00:13<00:02, 58.27it/s, loss=0.821]
+Epoch:  84%| | 835/1000 [00:13<00:02, 58.27it/s, loss=0.825]
+Epoch:  84%| | 842/1000 [00:13<00:02, 59.57it/s, loss=0.825]
+Epoch:  84%| | 842/1000 [00:13<00:02, 59.57it/s, loss=0.821]
+Epoch:  84%| | 842/1000 [00:13<00:02, 59.57it/s, loss=0.817]
+Epoch:  84%| | 842/1000 [00:13<00:02, 59.57it/s, loss=0.815]
+Epoch:  84%| | 842/1000 [00:13<00:02, 59.57it/s, loss=0.813]
+Epoch:  84%| | 842/1000 [00:13<00:02, 59.57it/s, loss=0.812]
+Epoch:  84%| | 842/1000 [00:13<00:02, 59.57it/s, loss=0.81] 
+Epoch:  84%| | 842/1000 [00:13<00:02, 59.57it/s, loss=0.811]
+Epoch:  85%| | 849/1000 [00:13<00:02, 61.35it/s, loss=0.811]
+Epoch:  85%| | 849/1000 [00:13<00:02, 61.35it/s, loss=0.812]
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+Epoch:  86%| | 856/1000 [00:13<00:02, 59.13it/s, loss=0.813]
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+Epoch:  86%| | 856/1000 [00:13<00:02, 59.13it/s, loss=0.811]
+Epoch:  86%| | 856/1000 [00:13<00:02, 59.13it/s, loss=0.809]
+Epoch:  86%| | 856/1000 [00:13<00:02, 59.13it/s, loss=0.807]
+Epoch:  86%| | 863/1000 [00:13<00:02, 59.82it/s, loss=0.807]
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+Epoch:  86%| | 863/1000 [00:13<00:02, 59.82it/s, loss=0.801]
+Epoch:  86%| | 863/1000 [00:13<00:02, 59.82it/s, loss=0.798]
+Epoch:  86%| | 863/1000 [00:13<00:02, 59.82it/s, loss=0.799]
+Epoch:  86%| | 863/1000 [00:13<00:02, 59.82it/s, loss=0.802]
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+Epoch:  87%| | 870/1000 [00:13<00:02, 57.93it/s, loss=0.803]
+Epoch:  87%| | 870/1000 [00:13<00:02, 57.93it/s, loss=0.803]
+Epoch:  87%| | 870/1000 [00:13<00:02, 57.93it/s, loss=0.804]
+Epoch:  87%| | 870/1000 [00:13<00:02, 57.93it/s, loss=0.806]
+Epoch:  87%| | 870/1000 [00:13<00:02, 57.93it/s, loss=0.807]
+Epoch:  87%| | 870/1000 [00:13<00:02, 57.93it/s, loss=0.806]
+Epoch:  88%| | 876/1000 [00:13<00:02, 57.41it/s, loss=0.806]
+Epoch:  88%| | 876/1000 [00:13<00:02, 57.41it/s, loss=0.805]
+Epoch:  88%| | 876/1000 [00:13<00:02, 57.41it/s, loss=0.803]
+Epoch:  88%| | 876/1000 [00:13<00:02, 57.41it/s, loss=0.802]
+Epoch:  88%| | 876/1000 [00:14<00:02, 57.41it/s, loss=0.805]
+Epoch:  88%| | 876/1000 [00:14<00:02, 57.41it/s, loss=0.804]
+Epoch:  88%| | 876/1000 [00:14<00:02, 57.41it/s, loss=0.807]
+Epoch:  88%| | 876/1000 [00:14<00:02, 57.41it/s, loss=0.806]
+Epoch:  88%| | 883/1000 [00:14<00:01, 59.25it/s, loss=0.806]
+Epoch:  88%| | 883/1000 [00:14<00:01, 59.25it/s, loss=0.804]
+Epoch:  88%| | 883/1000 [00:14<00:01, 59.25it/s, loss=0.805]
+Epoch:  88%| | 883/1000 [00:14<00:01, 59.25it/s, loss=0.805]
+Epoch:  88%| | 883/1000 [00:14<00:01, 59.25it/s, loss=0.804]
+Epoch:  88%| | 883/1000 [00:14<00:01, 59.25it/s, loss=0.802]
+Epoch:  88%| | 883/1000 [00:14<00:01, 59.25it/s, loss=0.802]
+Epoch:  89%| | 889/1000 [00:14<00:01, 57.76it/s, loss=0.802]
+Epoch:  89%| | 889/1000 [00:14<00:01, 57.76it/s, loss=0.802]
+Epoch:  89%| | 889/1000 [00:14<00:01, 57.76it/s, loss=0.803]
+Epoch:  89%| | 889/1000 [00:14<00:01, 57.76it/s, loss=0.802]
+Epoch:  89%| | 889/1000 [00:14<00:01, 57.76it/s, loss=0.802]
+Epoch:  89%| | 889/1000 [00:14<00:01, 57.76it/s, loss=0.8]  
+Epoch:  89%| | 889/1000 [00:14<00:01, 57.76it/s, loss=0.8]
+Epoch:  89%| | 889/1000 [00:14<00:01, 57.76it/s, loss=0.8]
+Epoch:  90%| | 896/1000 [00:14<00:01, 59.21it/s, loss=0.8]
+Epoch:  90%| | 896/1000 [00:14<00:01, 59.21it/s, loss=0.799]
+Epoch:  90%| | 896/1000 [00:14<00:01, 59.21it/s, loss=0.798]
+Epoch:  90%| | 896/1000 [00:14<00:01, 59.21it/s, loss=0.797]
+Epoch:  90%| | 896/1000 [00:14<00:01, 59.21it/s, loss=0.799]
+Epoch:  90%| | 896/1000 [00:14<00:01, 59.21it/s, loss=0.741]
+Epoch:  90%| | 896/1000 [00:14<00:01, 59.21it/s, loss=0.681]
+Epoch:  90%| | 896/1000 [00:14<00:01, 59.21it/s, loss=0.658]
+Epoch:  90%| | 903/1000 [00:14<00:01, 60.62it/s, loss=0.658]
+Epoch:  90%| | 903/1000 [00:14<00:01, 60.62it/s, loss=0.704]
+Epoch:  90%| | 903/1000 [00:14<00:01, 60.62it/s, loss=0.756]
+Epoch:  90%| | 903/1000 [00:14<00:01, 60.62it/s, loss=0.755]
+Epoch:  90%| | 903/1000 [00:14<00:01, 60.62it/s, loss=0.756]
+Epoch:  90%| | 903/1000 [00:14<00:01, 60.62it/s, loss=0.771]
+Epoch:  90%| | 903/1000 [00:14<00:01, 60.62it/s, loss=0.762]
+Epoch:  90%| | 903/1000 [00:14<00:01, 60.62it/s, loss=0.752]
+Epoch:  91%| | 910/1000 [00:14<00:01, 60.30it/s, loss=0.752]
+Epoch:  91%| | 910/1000 [00:14<00:01, 60.30it/s, loss=0.745]
+Epoch:  91%| | 910/1000 [00:14<00:01, 60.30it/s, loss=0.73] 
+Epoch:  91%| | 910/1000 [00:14<00:01, 60.30it/s, loss=0.739]
+Epoch:  91%| | 910/1000 [00:14<00:01, 60.30it/s, loss=0.743]
+Epoch:  91%| | 910/1000 [00:14<00:01, 60.30it/s, loss=0.747]
+Epoch:  91%| | 910/1000 [00:14<00:01, 60.30it/s, loss=0.74] 
+Epoch:  91%| | 910/1000 [00:14<00:01, 60.30it/s, loss=0.737]
+Epoch:  92%|| 917/1000 [00:14<00:01, 62.12it/s, loss=0.737]
+Epoch:  92%|| 917/1000 [00:14<00:01, 62.12it/s, loss=0.729]
+Epoch:  92%|| 917/1000 [00:14<00:01, 62.12it/s, loss=0.723]
+Epoch:  92%|| 917/1000 [00:14<00:01, 62.12it/s, loss=0.731]
+Epoch:  92%|| 917/1000 [00:14<00:01, 62.12it/s, loss=0.734]
+Epoch:  92%|| 917/1000 [00:14<00:01, 62.12it/s, loss=0.738]
+Epoch:  92%|| 917/1000 [00:14<00:01, 62.12it/s, loss=0.736]
+Epoch:  92%|| 917/1000 [00:14<00:01, 62.12it/s, loss=0.736]
+Epoch:  92%|| 924/1000 [00:14<00:01, 59.90it/s, loss=0.736]
+Epoch:  92%|| 924/1000 [00:14<00:01, 59.90it/s, loss=0.732]
+Epoch:  92%|| 924/1000 [00:14<00:01, 59.90it/s, loss=0.728]
+Epoch:  92%|| 924/1000 [00:14<00:01, 59.90it/s, loss=0.734]
+Epoch:  92%|| 924/1000 [00:14<00:01, 59.90it/s, loss=0.735]
+Epoch:  92%|| 924/1000 [00:14<00:01, 59.90it/s, loss=0.733]
+Epoch:  92%|| 924/1000 [00:14<00:01, 59.90it/s, loss=0.734]
+Epoch:  92%|| 924/1000 [00:14<00:01, 59.90it/s, loss=0.741]
+Epoch:  93%|| 931/1000 [00:14<00:01, 59.81it/s, loss=0.741]
+Epoch:  93%|| 931/1000 [00:14<00:01, 59.81it/s, loss=0.742]
+Epoch:  93%|| 931/1000 [00:14<00:01, 59.81it/s, loss=0.744]
+Epoch:  93%|| 931/1000 [00:14<00:01, 59.81it/s, loss=0.745]
+Epoch:  93%|| 931/1000 [00:14<00:01, 59.81it/s, loss=0.741]
+Epoch:  93%|| 931/1000 [00:14<00:01, 59.81it/s, loss=0.741]
+Epoch:  93%|| 931/1000 [00:14<00:01, 59.81it/s, loss=0.744]
+Epoch:  93%|| 931/1000 [00:14<00:01, 59.81it/s, loss=0.742]
+Epoch:  94%|| 938/1000 [00:14<00:01, 58.12it/s, loss=0.742]
+Epoch:  94%|| 938/1000 [00:14<00:01, 58.12it/s, loss=0.74] 
+Epoch:  94%|| 938/1000 [00:15<00:01, 58.12it/s, loss=0.74]
+Epoch:  94%|| 938/1000 [00:15<00:01, 58.12it/s, loss=0.741]
+Epoch:  94%|| 938/1000 [00:15<00:01, 58.12it/s, loss=0.739]
+Epoch:  94%|| 938/1000 [00:15<00:01, 58.12it/s, loss=0.74] 
+Epoch:  94%|| 938/1000 [00:15<00:01, 58.12it/s, loss=0.74]
+Epoch:  94%|| 938/1000 [00:15<00:01, 58.12it/s, loss=0.738]
+Epoch:  94%|| 945/1000 [00:15<00:00, 59.60it/s, loss=0.738]
+Epoch:  94%|| 945/1000 [00:15<00:00, 59.60it/s, loss=0.742]
+Epoch:  94%|| 945/1000 [00:15<00:00, 59.60it/s, loss=0.739]
+Epoch:  94%|| 945/1000 [00:15<00:00, 59.60it/s, loss=0.737]
+Epoch:  94%|| 945/1000 [00:15<00:00, 59.60it/s, loss=0.737]
+Epoch:  94%|| 945/1000 [00:15<00:00, 59.60it/s, loss=0.734]
+Epoch:  94%|| 945/1000 [00:15<00:00, 59.60it/s, loss=0.732]
+Epoch:  95%|| 951/1000 [00:15<00:00, 58.52it/s, loss=0.732]
+Epoch:  95%|| 951/1000 [00:15<00:00, 58.52it/s, loss=0.735]
+Epoch:  95%|| 951/1000 [00:15<00:00, 58.52it/s, loss=0.734]
+Epoch:  95%|| 951/1000 [00:15<00:00, 58.52it/s, loss=0.738]
+Epoch:  95%|| 951/1000 [00:15<00:00, 58.52it/s, loss=0.736]
+Epoch:  95%|| 951/1000 [00:15<00:00, 58.52it/s, loss=0.736]
+Epoch:  95%|| 951/1000 [00:15<00:00, 58.52it/s, loss=0.737]
+Epoch:  95%|| 951/1000 [00:15<00:00, 58.52it/s, loss=0.737]
+Epoch:  96%|| 958/1000 [00:15<00:00, 59.01it/s, loss=0.737]
+Epoch:  96%|| 958/1000 [00:15<00:00, 59.01it/s, loss=0.741]
+Epoch:  96%|| 958/1000 [00:15<00:00, 59.01it/s, loss=0.739]
+Epoch:  96%|| 958/1000 [00:15<00:00, 59.01it/s, loss=0.738]
+Epoch:  96%|| 958/1000 [00:15<00:00, 59.01it/s, loss=0.737]
+Epoch:  96%|| 958/1000 [00:15<00:00, 59.01it/s, loss=0.738]
+Epoch:  96%|| 958/1000 [00:15<00:00, 59.01it/s, loss=0.738]
+Epoch:  96%|| 958/1000 [00:15<00:00, 59.01it/s, loss=0.743]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.99it/s, loss=0.743]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.99it/s, loss=0.745]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.99it/s, loss=0.744]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.99it/s, loss=0.744]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.99it/s, loss=0.743]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.99it/s, loss=0.742]
+Epoch:  96%|| 965/1000 [00:15<00:00, 58.99it/s, loss=0.742]
+Epoch:  97%|| 971/1000 [00:15<00:00, 54.88it/s, loss=0.742]
+Epoch:  97%|| 971/1000 [00:15<00:00, 54.88it/s, loss=0.741]
+Epoch:  97%|| 971/1000 [00:15<00:00, 54.88it/s, loss=0.747]
+Epoch:  97%|| 971/1000 [00:15<00:00, 54.88it/s, loss=0.747]
+Epoch:  97%|| 971/1000 [00:15<00:00, 54.88it/s, loss=0.746]
+Epoch:  97%|| 971/1000 [00:15<00:00, 54.88it/s, loss=0.749]
+Epoch:  97%|| 971/1000 [00:15<00:00, 54.88it/s, loss=0.748]
+Epoch:  97%|| 971/1000 [00:15<00:00, 54.88it/s, loss=0.747]
+Epoch:  98%|| 978/1000 [00:15<00:00, 58.05it/s, loss=0.747]
+Epoch:  98%|| 978/1000 [00:15<00:00, 58.05it/s, loss=0.747]
+Epoch:  98%|| 978/1000 [00:15<00:00, 58.05it/s, loss=0.746]
+Epoch:  98%|| 978/1000 [00:15<00:00, 58.05it/s, loss=0.745]
+Epoch:  98%|| 978/1000 [00:15<00:00, 58.05it/s, loss=0.751]
+Epoch:  98%|| 978/1000 [00:15<00:00, 58.05it/s, loss=0.75] 
+Epoch:  98%|| 978/1000 [00:15<00:00, 58.05it/s, loss=0.748]
+Epoch:  98%|| 978/1000 [00:15<00:00, 58.05it/s, loss=0.749]
+Epoch:  98%|| 985/1000 [00:15<00:00, 59.21it/s, loss=0.749]
+Epoch:  98%|| 985/1000 [00:15<00:00, 59.21it/s, loss=0.748]
+Epoch:  98%|| 985/1000 [00:15<00:00, 59.21it/s, loss=0.748]
+Epoch:  98%|| 985/1000 [00:15<00:00, 59.21it/s, loss=0.749]
+Epoch:  98%|| 985/1000 [00:15<00:00, 59.21it/s, loss=0.748]
+Epoch:  98%|| 985/1000 [00:15<00:00, 59.21it/s, loss=0.747]
+Epoch:  98%|| 985/1000 [00:15<00:00, 59.21it/s, loss=0.747]
+Epoch:  99%|| 991/1000 [00:15<00:00, 57.78it/s, loss=0.747]
+Epoch:  99%|| 991/1000 [00:15<00:00, 57.78it/s, loss=0.747]
+Epoch:  99%|| 991/1000 [00:15<00:00, 57.78it/s, loss=0.746]
+Epoch:  99%|| 991/1000 [00:15<00:00, 57.78it/s, loss=0.745]
+Epoch:  99%|| 991/1000 [00:15<00:00, 57.78it/s, loss=0.747]
+Epoch:  99%|| 991/1000 [00:15<00:00, 57.78it/s, loss=0.748]
+Epoch:  99%|| 991/1000 [00:16<00:00, 57.78it/s, loss=0.747]
+Epoch: 100%|| 997/1000 [00:16<00:00, 55.68it/s, loss=0.747]
+Epoch: 100%|| 997/1000 [00:16<00:00, 55.68it/s, loss=0.747]
+Epoch: 100%|| 997/1000 [00:16<00:00, 55.68it/s, loss=0.746]
+Epoch: 100%|| 997/1000 [00:16<00:00, 55.68it/s, loss=0.745]
+Epoch: 100%|| 1000/1000 [00:16<00:00, 62.29it/s, loss=0.745]
+PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/generic/test_ddpm.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/generic/test_ddpm.py::test_sample_hyperparams PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_fit[test_plugin0] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_fit[test_plugin1] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_fit[test_plugin2] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_generate[test_plugin0] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_generate[test_plugin1] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_generate[test_plugin2] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_generate_constraints[test_plugin0] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_generate_constraints[test_plugin1] PASSED
+tests/plugins/generic/test_dummy_sampler.py::test_plugin_generate_constraints[test_plugin2] PASSED
+tests/plugins/generic/test_goggle.py::test_plugin_sanity[test_plugin0] SKIPPED (Goggle dependencies not
+installed)
+tests/plugins/generic/test_goggle.py::test_plugin_name[test_plugin0] SKIPPED (Goggle dependencies not
+installed)
+tests/plugins/generic/test_goggle.py::test_plugin_type[test_plugin0] SKIPPED (Goggle dependencies not
+installed)
+tests/plugins/generic/test_goggle.py::test_plugin_hyperparams[test_plugin0] SKIPPED (Goggle dependencies not
+installed)
+tests/plugins/generic/test_goggle.py::test_plugin_fit[test_plugin0] SKIPPED (Goggle dependencies not
+installed)
+tests/plugins/generic/test_goggle.py::test_plugin_generate[True-test_plugin0] SKIPPED (Goggle dependencies not
+installed)
+tests/plugins/generic/test_goggle.py::test_plugin_generate[False-test_plugin0] SKIPPED (Goggle dependencies
+not installed)
+tests/plugins/generic/test_goggle.py::test_plugin_generate_constraints_goggle[test_plugin0] SKIPPED (Goggle
+dependencies not installed)
+tests/plugins/generic/test_goggle.py::test_sample_hyperparams SKIPPED (Goggle dependencies not installed)
+tests/plugins/generic/test_great.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/generic/test_great.py::test_plugin_sanity[test_plugin1] PASSED
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+tests/plugins/generic/test_great.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/generic/test_great.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/generic/test_great.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/generic/test_great.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/generic/test_great.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/generic/test_great.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/generic/test_great.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/generic/test_great.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/generic/test_great.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/generic/test_great.py::test_plugin_fit[test_plugin0] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
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+100%|| 500/500 [10:20<00:00,  1.14s/it]
+                                                 
+
+100%|| 500/500 [10:20<00:00,  1.14s/it]
+                                                 
+
+100%|| 500/500 [10:21<00:00,  1.14s/it]
+100%|| 500/500 [10:21<00:00,  1.24s/it]
+{'loss': 0.4146, 'grad_norm': 4.007715702056885, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 621.8239, 'train_samples_per_second': 12.061, 'train_steps_per_second': 0.804, 'train_loss': 0.41457177734375, 'epoch': 50.0}
+PASSED
+tests/plugins/generic/test_great.py::test_plugin_fit[test_plugin1] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<11:19,  1.36s/it]
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+ 10%|         | 51/500 [01:03<08:52,  1.19s/it]
+ 10%|         | 52/500 [01:04<09:03,  1.21s/it]
+ 11%|         | 53/500 [01:06<09:18,  1.25s/it]
+ 11%|         | 54/500 [01:07<09:24,  1.27s/it]
+ 11%|         | 55/500 [01:08<09:32,  1.29s/it]
+ 11%|         | 56/500 [01:10<09:32,  1.29s/it]
+ 11%|        | 57/500 [01:11<09:45,  1.32s/it]
+ 12%|        | 58/500 [01:12<09:39,  1.31s/it]
+ 12%|        | 59/500 [01:14<09:45,  1.33s/it]
+ 12%|        | 60/500 [01:14<08:24,  1.15s/it]
+ 12%|        | 61/500 [01:16<08:47,  1.20s/it]
+ 12%|        | 62/500 [01:17<09:00,  1.23s/it]
+ 13%|        | 63/500 [01:18<09:05,  1.25s/it]
+ 13%|        | 64/500 [01:20<09:09,  1.26s/it]
+ 13%|        | 65/500 [01:21<09:14,  1.28s/it]
+ 13%|        | 66/500 [01:22<09:17,  1.28s/it]
+ 13%|        | 67/500 [01:24<09:29,  1.32s/it]
+ 14%|        | 68/500 [01:25<09:27,  1.31s/it]
+ 14%|        | 69/500 [01:26<09:25,  1.31s/it]
+ 14%|        | 70/500 [01:27<08:08,  1.14s/it]
+ 14%|        | 71/500 [01:28<08:23,  1.17s/it]
+ 14%|        | 72/500 [01:29<08:38,  1.21s/it]
+ 15%|        | 73/500 [01:31<08:45,  1.23s/it]
+ 15%|        | 74/500 [01:32<08:52,  1.25s/it]
+ 15%|        | 75/500 [01:33<08:47,  1.24s/it]
+ 15%|        | 76/500 [01:35<08:49,  1.25s/it]
+ 15%|        | 77/500 [01:36<08:53,  1.26s/it]
+ 16%|        | 78/500 [01:37<08:56,  1.27s/it]
+ 16%|        | 79/500 [01:38<08:55,  1.27s/it]
+ 16%|        | 80/500 [01:39<07:47,  1.11s/it]
+ 16%|        | 81/500 [01:40<08:06,  1.16s/it]
+ 16%|        | 82/500 [01:42<08:20,  1.20s/it]
+ 17%|        | 83/500 [01:43<08:32,  1.23s/it]
+ 17%|        | 84/500 [01:44<08:41,  1.25s/it]
+ 17%|        | 85/500 [01:46<08:52,  1.28s/it]
+ 17%|        | 86/500 [01:47<08:52,  1.29s/it]
+ 17%|        | 87/500 [01:48<08:50,  1.28s/it]
+ 18%|        | 88/500 [01:50<08:50,  1.29s/it]
+ 18%|        | 89/500 [01:51<08:49,  1.29s/it]
+ 18%|        | 90/500 [01:52<07:39,  1.12s/it]
+ 18%|        | 91/500 [01:53<08:01,  1.18s/it]
+ 18%|        | 92/500 [01:54<08:12,  1.21s/it]
+ 19%|        | 93/500 [01:55<08:22,  1.24s/it]
+ 19%|        | 94/500 [01:57<08:25,  1.25s/it]
+ 19%|        | 95/500 [01:58<08:29,  1.26s/it]
+ 19%|        | 96/500 [01:59<08:34,  1.27s/it]
+ 19%|        | 97/500 [02:01<08:38,  1.29s/it]
+ 20%|        | 98/500 [02:02<08:49,  1.32s/it]
+ 20%|        | 99/500 [02:03<08:47,  1.32s/it]
+ 20%|        | 100/500 [02:04<07:35,  1.14s/it]
+ 20%|        | 101/500 [02:05<07:51,  1.18s/it]
+ 20%|        | 102/500 [02:07<08:01,  1.21s/it]
+ 21%|        | 103/500 [02:08<08:11,  1.24s/it]
+ 21%|        | 104/500 [02:09<08:16,  1.25s/it]
+ 21%|        | 105/500 [02:11<08:20,  1.27s/it]
+ 21%|        | 106/500 [02:12<08:24,  1.28s/it]
+ 21%|       | 107/500 [02:13<08:29,  1.30s/it]
+ 22%|       | 108/500 [02:14<08:33,  1.31s/it]
+ 22%|       | 109/500 [02:16<08:31,  1.31s/it]
+ 22%|       | 110/500 [02:17<07:19,  1.13s/it]
+ 22%|       | 111/500 [02:18<07:36,  1.17s/it]
+ 22%|       | 112/500 [02:19<07:48,  1.21s/it]
+ 23%|       | 113/500 [02:20<08:02,  1.25s/it]
+ 23%|       | 114/500 [02:22<08:08,  1.27s/it]
+ 23%|       | 115/500 [02:23<08:08,  1.27s/it]
+ 23%|       | 116/500 [02:24<08:06,  1.27s/it]
+ 23%|       | 117/500 [02:26<08:09,  1.28s/it]
+ 24%|       | 118/500 [02:27<08:07,  1.28s/it]
+ 24%|       | 119/500 [02:28<08:10,  1.29s/it]
+ 24%|       | 120/500 [02:29<07:05,  1.12s/it]
+ 24%|       | 121/500 [02:30<07:19,  1.16s/it]
+ 24%|       | 122/500 [02:31<07:31,  1.19s/it]
+ 25%|       | 123/500 [02:33<07:43,  1.23s/it]
+ 25%|       | 124/500 [02:34<07:45,  1.24s/it]
+ 25%|       | 125/500 [02:35<07:54,  1.27s/it]
+ 25%|       | 126/500 [02:37<07:55,  1.27s/it]
+ 25%|       | 127/500 [02:38<07:58,  1.28s/it]
+ 26%|       | 128/500 [02:39<07:54,  1.28s/it]
+ 26%|       | 129/500 [02:41<08:01,  1.30s/it]
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+ 26%|       | 131/500 [02:43<07:19,  1.19s/it]
+ 26%|       | 132/500 [02:44<07:36,  1.24s/it]
+ 27%|       | 133/500 [02:45<07:39,  1.25s/it]
+ 27%|       | 134/500 [02:47<07:49,  1.28s/it]
+ 27%|       | 135/500 [02:48<07:56,  1.31s/it]
+ 27%|       | 136/500 [02:49<07:48,  1.29s/it]
+ 27%|       | 137/500 [02:50<07:45,  1.28s/it]
+ 28%|       | 138/500 [02:52<07:47,  1.29s/it]
+ 28%|       | 139/500 [02:53<07:45,  1.29s/it]
+ 28%|       | 140/500 [02:54<06:45,  1.13s/it]
+ 28%|       | 141/500 [02:55<07:01,  1.17s/it]
+ 28%|       | 142/500 [02:56<07:11,  1.21s/it]
+ 29%|       | 143/500 [02:58<07:20,  1.23s/it]
+ 29%|       | 144/500 [02:59<07:24,  1.25s/it]
+ 29%|       | 145/500 [03:00<07:23,  1.25s/it]
+ 29%|       | 146/500 [03:02<07:29,  1.27s/it]
+ 29%|       | 147/500 [03:03<07:36,  1.29s/it]
+ 30%|       | 148/500 [03:04<07:36,  1.30s/it]
+ 30%|       | 149/500 [03:05<07:36,  1.30s/it]
+ 30%|       | 150/500 [03:06<06:35,  1.13s/it]
+ 30%|       | 151/500 [03:07<06:51,  1.18s/it]
+ 30%|       | 152/500 [03:09<07:01,  1.21s/it]
+ 31%|       | 153/500 [03:10<07:08,  1.23s/it]
+ 31%|       | 154/500 [03:11<07:12,  1.25s/it]
+ 31%|       | 155/500 [03:13<07:18,  1.27s/it]
+ 31%|       | 156/500 [03:14<07:20,  1.28s/it]
+ 31%|      | 157/500 [03:15<07:26,  1.30s/it]
+ 32%|      | 158/500 [03:17<07:26,  1.31s/it]
+ 32%|      | 159/500 [03:18<07:23,  1.30s/it]
+ 32%|      | 160/500 [03:19<06:24,  1.13s/it]
+ 32%|      | 161/500 [03:20<06:44,  1.19s/it]
+ 32%|      | 162/500 [03:21<06:49,  1.21s/it]
+ 33%|      | 163/500 [03:23<07:00,  1.25s/it]
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+ 33%|      | 166/500 [03:26<07:09,  1.29s/it]
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+ 34%|      | 168/500 [03:29<07:09,  1.29s/it]
+ 34%|      | 169/500 [03:30<07:08,  1.30s/it]
+ 34%|      | 170/500 [03:31<06:11,  1.13s/it]
+ 34%|      | 171/500 [03:32<06:31,  1.19s/it]
+ 34%|      | 172/500 [03:34<06:43,  1.23s/it]
+ 35%|      | 173/500 [03:35<06:52,  1.26s/it]
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+ 35%|      | 177/500 [03:40<06:58,  1.30s/it]
+ 36%|      | 178/500 [03:42<06:57,  1.30s/it]
+ 36%|      | 179/500 [03:43<06:57,  1.30s/it]
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+ 44%|     | 218/500 [04:31<06:03,  1.29s/it]
+ 44%|     | 219/500 [04:33<06:05,  1.30s/it]
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+ 68%|   | 342/500 [07:05<03:11,  1.22s/it]
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+ 99%|| 497/500 [10:18<00:03,  1.28s/it]
+100%|| 498/500 [10:19<00:02,  1.28s/it]
+100%|| 499/500 [10:20<00:01,  1.29s/it]
+100%|| 500/500 [10:21<00:00,  1.12s/it]
+                                                 
+
+100%|| 500/500 [10:21<00:00,  1.12s/it]
+                                                 
+
+100%|| 500/500 [10:23<00:00,  1.12s/it]
+100%|| 500/500 [10:23<00:00,  1.25s/it]
+{'loss': 0.4146, 'grad_norm': 4.007715702056885, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 623.1218, 'train_samples_per_second': 12.036, 'train_steps_per_second': 0.802, 'train_loss': 0.41457177734375, 'epoch': 50.0}
+PASSED
+tests/plugins/generic/test_great.py::test_plugin_fit[test_plugin2] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<12:03,  1.45s/it]
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+ 25%|       | 123/500 [02:33<07:43,  1.23s/it]
+ 25%|       | 124/500 [02:35<07:50,  1.25s/it]
+ 25%|       | 125/500 [02:36<07:56,  1.27s/it]
+ 25%|       | 126/500 [02:37<07:56,  1.28s/it]
+ 25%|       | 127/500 [02:38<07:58,  1.28s/it]
+ 26%|       | 128/500 [02:40<08:00,  1.29s/it]
+ 26%|       | 129/500 [02:41<08:01,  1.30s/it]
+ 26%|       | 130/500 [02:42<06:58,  1.13s/it]
+ 26%|       | 131/500 [02:43<07:15,  1.18s/it]
+ 26%|       | 132/500 [02:44<07:28,  1.22s/it]
+ 27%|       | 133/500 [02:46<07:34,  1.24s/it]
+ 27%|       | 134/500 [02:47<07:42,  1.26s/it]
+ 27%|       | 135/500 [02:48<07:44,  1.27s/it]
+ 27%|       | 136/500 [02:50<07:45,  1.28s/it]
+ 27%|       | 137/500 [02:51<07:46,  1.28s/it]
+ 28%|       | 138/500 [02:52<07:45,  1.29s/it]
+ 28%|       | 139/500 [02:53<07:43,  1.28s/it]
+ 28%|       | 140/500 [02:54<06:46,  1.13s/it]
+ 28%|       | 141/500 [02:56<07:07,  1.19s/it]
+ 28%|       | 142/500 [02:57<07:18,  1.22s/it]
+ 29%|       | 143/500 [02:58<07:24,  1.25s/it]
+ 29%|       | 144/500 [03:00<07:30,  1.26s/it]
+ 29%|       | 145/500 [03:01<07:34,  1.28s/it]
+ 29%|       | 146/500 [03:02<07:38,  1.30s/it]
+ 29%|       | 147/500 [03:04<07:44,  1.32s/it]
+ 30%|       | 148/500 [03:05<07:41,  1.31s/it]
+ 30%|       | 149/500 [03:06<07:36,  1.30s/it]
+ 30%|       | 150/500 [03:07<06:38,  1.14s/it]
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+ 30%|       | 152/500 [03:10<07:11,  1.24s/it]
+ 31%|       | 153/500 [03:11<07:19,  1.27s/it]
+ 31%|       | 154/500 [03:12<07:23,  1.28s/it]
+ 31%|       | 155/500 [03:13<07:24,  1.29s/it]
+ 31%|       | 156/500 [03:15<07:23,  1.29s/it]
+ 31%|      | 157/500 [03:16<07:20,  1.28s/it]
+ 32%|      | 158/500 [03:17<07:20,  1.29s/it]
+ 32%|      | 159/500 [03:19<07:19,  1.29s/it]
+ 32%|      | 160/500 [03:19<06:25,  1.13s/it]
+ 32%|      | 161/500 [03:21<06:39,  1.18s/it]
+ 32%|      | 162/500 [03:22<06:51,  1.22s/it]
+ 33%|      | 163/500 [03:23<06:58,  1.24s/it]
+ 33%|      | 164/500 [03:25<06:56,  1.24s/it]
+ 33%|      | 165/500 [03:26<06:59,  1.25s/it]
+ 33%|      | 166/500 [03:27<06:58,  1.25s/it]
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+ 34%|      | 169/500 [03:31<06:58,  1.27s/it]
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+ 35%|      | 173/500 [03:36<06:48,  1.25s/it]
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+ 99%|| 497/500 [10:21<00:03,  1.28s/it]
+100%|| 498/500 [10:23<00:02,  1.28s/it]
+100%|| 499/500 [10:24<00:01,  1.28s/it]
+100%|| 500/500 [10:25<00:00,  1.11s/it]
+                                                 
+
+100%|| 500/500 [10:25<00:00,  1.11s/it]
+                                                 
+
+100%|| 500/500 [10:26<00:00,  1.11s/it]
+100%|| 500/500 [10:26<00:00,  1.25s/it]
+{'loss': 0.4146, 'grad_norm': 4.007715702056885, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 626.6655, 'train_samples_per_second': 11.968, 'train_steps_per_second': 0.798, 'train_loss': 0.41457177734375, 'epoch': 50.0}
+PASSED
+tests/plugins/generic/test_great.py::test_plugin_generate[True-test_plugin0] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<12:01,  1.45s/it]
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+ 39%|      | 195/500 [04:19<06:57,  1.37s/it]
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+ 40%|      | 198/500 [04:23<06:58,  1.39s/it]
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+ 98%|| 489/500 [10:50<00:15,  1.40s/it]
+ 98%|| 490/500 [10:51<00:12,  1.22s/it]
+ 98%|| 491/500 [10:52<00:11,  1.27s/it]
+ 98%|| 492/500 [10:54<00:10,  1.31s/it]
+ 99%|| 493/500 [10:55<00:09,  1.32s/it]
+ 99%|| 494/500 [10:57<00:08,  1.35s/it]
+ 99%|| 495/500 [10:58<00:06,  1.35s/it]
+ 99%|| 496/500 [10:59<00:05,  1.39s/it]
+ 99%|| 497/500 [11:01<00:04,  1.37s/it]
+100%|| 498/500 [11:02<00:02,  1.37s/it]
+100%|| 499/500 [11:03<00:01,  1.36s/it]
+100%|| 500/500 [11:04<00:00,  1.19s/it]
+                                                 
+
+100%|| 500/500 [11:04<00:00,  1.19s/it]
+                                                 
+
+100%|| 500/500 [11:06<00:00,  1.19s/it]
+100%|| 500/500 [11:06<00:00,  1.33s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 666.2623, 'train_samples_per_second': 11.257, 'train_steps_per_second': 0.75, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 67%|   | 100/150 [00:30<00:15,  3.24it/s]
+200it [01:01,  3.26it/s]                         
+200it [01:01,  3.25it/s]
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 66%|   | 99/150 [00:30<00:15,  3.23it/s]
+198it [01:01,  3.23it/s]                        
+198it [01:01,  3.23it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.28it/s]             
+100it [00:30,  3.28it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.28it/s]             
+100it [00:30,  3.28it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.28it/s]             
+100it [00:30,  3.28it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.27it/s]             
+100it [00:30,  3.27it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.28it/s]             
+100it [00:30,  3.28it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+98it [00:30,  3.19it/s]              
+98it [00:30,  3.19it/s]
+PASSED
+tests/plugins/generic/test_great.py::test_plugin_generate[True-test_plugin1] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
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+                                                 
+
+100%|| 500/500 [11:03<00:00,  1.20s/it]
+                                                 
+
+100%|| 500/500 [11:04<00:00,  1.20s/it]
+100%|| 500/500 [11:04<00:00,  1.33s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 664.9267, 'train_samples_per_second': 11.279, 'train_steps_per_second': 0.752, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 67%|   | 100/150 [00:30<00:15,  3.26it/s]
+200it [01:01,  3.26it/s]                         
+200it [01:01,  3.26it/s]
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 66%|   | 99/150 [00:30<00:15,  3.21it/s]
+198it [01:01,  3.23it/s]                        
+198it [01:01,  3.23it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
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+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
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+100it [00:30,  3.27it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+98it [00:30,  3.21it/s]              
+98it [00:30,  3.21it/s]
+PASSED
+tests/plugins/generic/test_great.py::test_plugin_generate[True-test_plugin2] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<11:22,  1.37s/it]
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+  1%|          | 6/500 [00:08<11:24,  1.38s/it]
+  1%|         | 7/500 [00:09<11:25,  1.39s/it]
+  2%|         | 8/500 [00:11<11:28,  1.40s/it]
+  2%|         | 9/500 [00:12<11:26,  1.40s/it]
+  2%|         | 10/500 [00:13<09:57,  1.22s/it]
+  2%|         | 11/500 [00:14<10:18,  1.27s/it]
+  2%|         | 12/500 [00:16<10:35,  1.30s/it]
+  3%|         | 13/500 [00:17<10:40,  1.31s/it]
+  3%|         | 14/500 [00:18<10:46,  1.33s/it]
+  3%|         | 15/500 [00:20<10:50,  1.34s/it]
+  3%|         | 16/500 [00:21<10:56,  1.36s/it]
+  3%|         | 17/500 [00:22<10:57,  1.36s/it]
+  4%|         | 18/500 [00:24<10:57,  1.36s/it]
+  4%|         | 19/500 [00:25<11:04,  1.38s/it]
+  4%|         | 20/500 [00:26<09:41,  1.21s/it]
+  4%|         | 21/500 [00:27<10:09,  1.27s/it]
+  4%|         | 22/500 [00:29<10:21,  1.30s/it]
+  5%|         | 23/500 [00:30<10:36,  1.33s/it]
+  5%|         | 24/500 [00:32<10:40,  1.35s/it]
+  5%|         | 25/500 [00:33<10:46,  1.36s/it]
+  5%|         | 26/500 [00:34<10:51,  1.37s/it]
+  5%|         | 27/500 [00:36<10:45,  1.36s/it]
+  6%|         | 28/500 [00:37<10:45,  1.37s/it]
+  6%|         | 29/500 [00:38<10:39,  1.36s/it]
+  6%|         | 30/500 [00:39<09:18,  1.19s/it]
+  6%|         | 31/500 [00:41<09:38,  1.23s/it]
+  6%|         | 32/500 [00:42<09:57,  1.28s/it]
+  7%|         | 33/500 [00:43<10:08,  1.30s/it]
+  7%|         | 34/500 [00:45<10:18,  1.33s/it]
+  7%|         | 35/500 [00:46<10:34,  1.36s/it]
+  7%|         | 36/500 [00:48<10:35,  1.37s/it]
+  7%|         | 37/500 [00:49<10:37,  1.38s/it]
+  8%|         | 38/500 [00:50<10:38,  1.38s/it]
+  8%|         | 39/500 [00:52<10:34,  1.38s/it]
+  8%|         | 40/500 [00:53<09:17,  1.21s/it]
+  8%|         | 41/500 [00:54<09:34,  1.25s/it]
+  8%|         | 42/500 [00:55<09:48,  1.28s/it]
+  9%|         | 43/500 [00:57<09:58,  1.31s/it]
+  9%|         | 44/500 [00:58<10:02,  1.32s/it]
+  9%|         | 45/500 [00:59<10:04,  1.33s/it]
+  9%|         | 46/500 [01:01<10:09,  1.34s/it]
+  9%|         | 47/500 [01:02<10:18,  1.37s/it]
+ 10%|         | 48/500 [01:03<10:16,  1.37s/it]
+ 10%|         | 49/500 [01:05<10:21,  1.38s/it]
+ 10%|         | 50/500 [01:06<09:00,  1.20s/it]
+ 10%|         | 51/500 [01:07<09:17,  1.24s/it]
+ 10%|         | 52/500 [01:08<09:34,  1.28s/it]
+ 11%|         | 53/500 [01:10<09:39,  1.30s/it]
+ 11%|         | 54/500 [01:11<09:49,  1.32s/it]
+ 11%|         | 55/500 [01:12<09:52,  1.33s/it]
+ 11%|         | 56/500 [01:14<10:11,  1.38s/it]
+ 11%|        | 57/500 [01:15<10:09,  1.38s/it]
+ 12%|        | 58/500 [01:17<10:14,  1.39s/it]
+ 12%|        | 59/500 [01:18<10:14,  1.39s/it]
+ 12%|        | 60/500 [01:19<08:54,  1.21s/it]
+ 12%|        | 61/500 [01:20<09:12,  1.26s/it]
+ 12%|        | 62/500 [01:22<09:23,  1.29s/it]
+ 13%|        | 63/500 [01:23<09:33,  1.31s/it]
+ 13%|        | 64/500 [01:24<09:41,  1.33s/it]
+ 13%|        | 65/500 [01:26<09:53,  1.36s/it]
+ 13%|        | 66/500 [01:27<09:53,  1.37s/it]
+ 13%|        | 67/500 [01:29<09:56,  1.38s/it]
+ 14%|        | 68/500 [01:30<09:51,  1.37s/it]
+ 14%|        | 69/500 [01:31<09:50,  1.37s/it]
+ 14%|        | 70/500 [01:32<08:36,  1.20s/it]
+ 14%|        | 71/500 [01:33<08:53,  1.24s/it]
+ 14%|        | 72/500 [01:35<09:13,  1.29s/it]
+ 15%|        | 73/500 [01:36<09:23,  1.32s/it]
+ 15%|        | 74/500 [01:38<09:38,  1.36s/it]
+ 15%|        | 75/500 [01:39<09:40,  1.37s/it]
+ 15%|        | 76/500 [01:40<09:38,  1.36s/it]
+ 15%|        | 77/500 [01:42<09:35,  1.36s/it]
+ 16%|        | 78/500 [01:43<09:36,  1.37s/it]
+ 16%|        | 79/500 [01:45<09:35,  1.37s/it]
+ 16%|        | 80/500 [01:45<08:22,  1.20s/it]
+ 16%|        | 81/500 [01:47<08:39,  1.24s/it]
+ 16%|        | 82/500 [01:48<08:54,  1.28s/it]
+ 17%|        | 83/500 [01:49<09:10,  1.32s/it]
+ 17%|        | 84/500 [01:51<09:17,  1.34s/it]
+ 17%|        | 85/500 [01:52<09:20,  1.35s/it]
+ 17%|        | 86/500 [01:54<09:24,  1.36s/it]
+ 17%|        | 87/500 [01:55<09:25,  1.37s/it]
+ 18%|        | 88/500 [01:56<09:25,  1.37s/it]
+ 18%|        | 89/500 [01:58<09:22,  1.37s/it]
+ 18%|        | 90/500 [01:59<08:14,  1.21s/it]
+ 18%|        | 91/500 [02:00<08:29,  1.24s/it]
+ 18%|        | 92/500 [02:01<08:42,  1.28s/it]
+ 19%|        | 93/500 [02:03<08:57,  1.32s/it]
+ 19%|        | 94/500 [02:04<09:07,  1.35s/it]
+ 19%|        | 95/500 [02:05<09:06,  1.35s/it]
+ 19%|        | 96/500 [02:07<09:15,  1.38s/it]
+ 19%|        | 97/500 [02:08<09:15,  1.38s/it]
+ 20%|        | 98/500 [02:10<09:11,  1.37s/it]
+ 20%|        | 99/500 [02:11<09:09,  1.37s/it]
+ 20%|        | 100/500 [02:12<07:52,  1.18s/it]
+ 20%|        | 101/500 [02:13<08:21,  1.26s/it]
+ 20%|        | 102/500 [02:15<08:39,  1.30s/it]
+ 21%|        | 103/500 [02:16<08:48,  1.33s/it]
+ 21%|        | 104/500 [02:17<08:47,  1.33s/it]
+ 21%|        | 105/500 [02:19<08:50,  1.34s/it]
+ 21%|        | 106/500 [02:20<08:59,  1.37s/it]
+ 21%|       | 107/500 [02:21<08:55,  1.36s/it]
+ 22%|       | 108/500 [02:23<08:59,  1.38s/it]
+ 22%|       | 109/500 [02:24<09:00,  1.38s/it]
+ 22%|       | 110/500 [02:25<07:48,  1.20s/it]
+ 22%|       | 111/500 [02:26<08:09,  1.26s/it]
+ 22%|       | 112/500 [02:28<08:21,  1.29s/it]
+ 23%|       | 113/500 [02:29<08:28,  1.31s/it]
+ 23%|       | 114/500 [02:31<08:31,  1.32s/it]
+ 23%|       | 115/500 [02:32<08:36,  1.34s/it]
+ 23%|       | 116/500 [02:33<08:43,  1.36s/it]
+ 23%|       | 117/500 [02:35<08:48,  1.38s/it]
+ 24%|       | 118/500 [02:36<08:46,  1.38s/it]
+ 24%|       | 119/500 [02:37<08:44,  1.38s/it]
+ 24%|       | 120/500 [02:38<07:39,  1.21s/it]
+ 24%|       | 121/500 [02:40<07:55,  1.25s/it]
+ 24%|       | 122/500 [02:41<08:05,  1.29s/it]
+ 25%|       | 123/500 [02:42<08:18,  1.32s/it]
+ 25%|       | 124/500 [02:44<08:26,  1.35s/it]
+ 25%|       | 125/500 [02:45<08:22,  1.34s/it]
+ 25%|       | 126/500 [02:47<08:27,  1.36s/it]
+ 25%|       | 127/500 [02:48<08:28,  1.36s/it]
+ 26%|       | 128/500 [02:49<08:33,  1.38s/it]
+ 26%|       | 129/500 [02:51<08:32,  1.38s/it]
+ 26%|       | 130/500 [02:51<07:24,  1.20s/it]
+ 26%|       | 131/500 [02:53<07:40,  1.25s/it]
+ 26%|       | 132/500 [02:54<07:58,  1.30s/it]
+ 27%|       | 133/500 [02:56<08:05,  1.32s/it]
+ 27%|       | 134/500 [02:57<08:13,  1.35s/it]
+ 27%|       | 135/500 [02:58<08:14,  1.36s/it]
+ 27%|       | 136/500 [03:00<08:17,  1.37s/it]
+ 27%|       | 137/500 [03:01<08:25,  1.39s/it]
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+ 28%|       | 139/500 [03:04<08:17,  1.38s/it]
+ 28%|       | 140/500 [03:05<07:14,  1.21s/it]
+ 28%|       | 141/500 [03:06<07:37,  1.27s/it]
+ 28%|       | 142/500 [03:08<07:46,  1.30s/it]
+ 29%|       | 143/500 [03:09<07:58,  1.34s/it]
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+ 29%|       | 145/500 [03:12<08:12,  1.39s/it]
+ 29%|       | 146/500 [03:13<08:09,  1.38s/it]
+ 29%|       | 147/500 [03:15<08:11,  1.39s/it]
+ 30%|       | 148/500 [03:16<08:05,  1.38s/it]
+ 30%|       | 149/500 [03:17<08:02,  1.38s/it]
+ 30%|       | 150/500 [03:18<07:01,  1.20s/it]
+ 30%|       | 151/500 [03:20<07:17,  1.25s/it]
+ 30%|       | 152/500 [03:21<07:26,  1.28s/it]
+ 31%|       | 153/500 [03:22<07:34,  1.31s/it]
+ 31%|       | 154/500 [03:24<07:48,  1.35s/it]
+ 31%|       | 155/500 [03:25<07:49,  1.36s/it]
+ 31%|       | 156/500 [03:27<07:50,  1.37s/it]
+ 31%|      | 157/500 [03:28<07:54,  1.38s/it]
+ 32%|      | 158/500 [03:29<07:50,  1.38s/it]
+ 32%|      | 159/500 [03:31<07:47,  1.37s/it]
+ 32%|      | 160/500 [03:31<06:48,  1.20s/it]
+ 32%|      | 161/500 [03:33<07:08,  1.26s/it]
+ 32%|      | 162/500 [03:34<07:15,  1.29s/it]
+ 33%|      | 163/500 [03:36<07:23,  1.32s/it]
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+ 55%|    | 276/500 [06:05<05:06,  1.37s/it]
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+ 57%|    | 284/500 [06:16<04:49,  1.34s/it]
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+ 57%|    | 286/500 [06:19<04:50,  1.36s/it]
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+ 59%|    | 295/500 [06:30<04:38,  1.36s/it]
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+100%|| 498/500 [11:00<00:02,  1.37s/it]
+100%|| 499/500 [11:01<00:01,  1.39s/it]
+100%|| 500/500 [11:02<00:00,  1.22s/it]
+                                                 
+
+100%|| 500/500 [11:02<00:00,  1.22s/it]
+                                                 
+
+100%|| 500/500 [11:04<00:00,  1.22s/it]
+100%|| 500/500 [11:04<00:00,  1.33s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 664.2988, 'train_samples_per_second': 11.29, 'train_steps_per_second': 0.753, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 67%|   | 100/150 [00:30<00:15,  3.26it/s]
+200it [01:01,  3.25it/s]                         
+200it [01:01,  3.26it/s]
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 66%|   | 99/150 [00:30<00:15,  3.22it/s]
+198it [01:01,  3.22it/s]                        
+198it [01:01,  3.22it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+98it [00:30,  3.20it/s]              
+98it [00:30,  3.20it/s]
+PASSED
+tests/plugins/generic/test_great.py::test_plugin_generate[False-test_plugin0] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<11:01,  1.33s/it]
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+ 11%|         | 55/500 [01:11<09:53,  1.33s/it]
+ 11%|         | 56/500 [01:13<09:51,  1.33s/it]
+ 11%|        | 57/500 [01:14<09:49,  1.33s/it]
+ 12%|        | 58/500 [01:15<09:50,  1.34s/it]
+ 12%|        | 59/500 [01:17<09:49,  1.34s/it]
+ 12%|        | 60/500 [01:17<08:31,  1.16s/it]
+ 12%|        | 61/500 [01:19<08:56,  1.22s/it]
+ 12%|        | 62/500 [01:20<09:14,  1.27s/it]
+ 13%|        | 63/500 [01:21<09:29,  1.30s/it]
+ 13%|        | 64/500 [01:23<09:35,  1.32s/it]
+ 13%|        | 65/500 [01:24<09:40,  1.33s/it]
+ 13%|        | 66/500 [01:26<09:39,  1.34s/it]
+ 13%|        | 67/500 [01:27<09:36,  1.33s/it]
+ 14%|        | 68/500 [01:28<09:33,  1.33s/it]
+ 14%|        | 69/500 [01:29<09:35,  1.33s/it]
+ 14%|        | 70/500 [01:30<08:18,  1.16s/it]
+ 14%|        | 71/500 [01:32<08:41,  1.21s/it]
+ 14%|        | 72/500 [01:33<08:59,  1.26s/it]
+ 15%|        | 73/500 [01:34<09:07,  1.28s/it]
+ 15%|        | 74/500 [01:36<09:11,  1.30s/it]
+ 15%|        | 75/500 [01:37<09:17,  1.31s/it]
+ 15%|        | 76/500 [01:38<09:21,  1.32s/it]
+ 15%|        | 77/500 [01:40<09:20,  1.32s/it]
+ 16%|        | 78/500 [01:41<09:23,  1.33s/it]
+ 16%|        | 79/500 [01:42<09:30,  1.35s/it]
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+ 18%|        | 88/500 [01:54<09:17,  1.35s/it]
+ 18%|        | 89/500 [01:55<09:19,  1.36s/it]
+ 18%|        | 90/500 [01:56<08:07,  1.19s/it]
+ 18%|        | 91/500 [01:58<08:29,  1.24s/it]
+ 18%|        | 92/500 [01:59<08:49,  1.30s/it]
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+ 19%|        | 96/500 [02:04<09:03,  1.35s/it]
+ 19%|        | 97/500 [02:06<09:06,  1.36s/it]
+ 20%|        | 98/500 [02:07<08:59,  1.34s/it]
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+ 20%|        | 102/500 [02:12<08:23,  1.26s/it]
+ 21%|        | 103/500 [02:13<08:33,  1.29s/it]
+ 21%|        | 104/500 [02:15<08:38,  1.31s/it]
+ 21%|        | 105/500 [02:16<08:44,  1.33s/it]
+ 21%|        | 106/500 [02:17<08:47,  1.34s/it]
+ 21%|       | 107/500 [02:19<08:50,  1.35s/it]
+ 22%|       | 108/500 [02:20<08:48,  1.35s/it]
+ 22%|       | 109/500 [02:21<08:45,  1.34s/it]
+ 22%|       | 110/500 [02:22<07:38,  1.18s/it]
+ 22%|       | 111/500 [02:24<08:00,  1.24s/it]
+ 22%|       | 112/500 [02:25<08:17,  1.28s/it]
+ 23%|       | 113/500 [02:26<08:21,  1.30s/it]
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+ 23%|       | 115/500 [02:29<08:34,  1.34s/it]
+ 23%|       | 116/500 [02:30<08:35,  1.34s/it]
+ 23%|       | 117/500 [02:32<08:36,  1.35s/it]
+ 24%|       | 118/500 [02:33<08:38,  1.36s/it]
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+ 26%|       | 128/500 [02:46<08:19,  1.34s/it]
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+ 30%|       | 148/500 [03:12<07:54,  1.35s/it]
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+ 31%|       | 153/500 [03:18<07:30,  1.30s/it]
+ 31%|       | 154/500 [03:20<07:35,  1.32s/it]
+ 31%|       | 155/500 [03:21<07:33,  1.32s/it]
+ 31%|       | 156/500 [03:22<07:36,  1.33s/it]
+ 31%|      | 157/500 [03:24<07:41,  1.35s/it]
+ 32%|      | 158/500 [03:25<07:42,  1.35s/it]
+ 32%|      | 159/500 [03:26<07:38,  1.34s/it]
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+ 33%|      | 163/500 [03:31<07:23,  1.32s/it]
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+ 99%|| 497/500 [10:45<00:04,  1.35s/it]
+100%|| 498/500 [10:46<00:02,  1.36s/it]
+100%|| 499/500 [10:47<00:01,  1.37s/it]
+100%|| 500/500 [10:48<00:00,  1.19s/it]
+                                                 
+
+100%|| 500/500 [10:48<00:00,  1.19s/it]
+                                                 
+
+100%|| 500/500 [10:50<00:00,  1.19s/it]
+100%|| 500/500 [10:50<00:00,  1.30s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 650.243, 'train_samples_per_second': 11.534, 'train_steps_per_second': 0.769, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 67%|   | 100/150 [00:30<00:15,  3.24it/s]
+200it [01:01,  3.25it/s]                         
+200it [01:01,  3.25it/s]
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 66%|   | 99/150 [00:30<00:15,  3.23it/s]
+198it [01:01,  3.23it/s]                        
+198it [01:01,  3.23it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.24it/s]             
+100it [00:30,  3.24it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+98it [00:30,  3.18it/s]              
+98it [00:30,  3.18it/s]
+PASSED
+tests/plugins/generic/test_great.py::test_plugin_generate[False-test_plugin1] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<11:37,  1.40s/it]
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+ 21%|        | 105/500 [02:16<08:42,  1.32s/it]
+ 21%|        | 106/500 [02:17<08:46,  1.34s/it]
+ 21%|       | 107/500 [02:19<08:47,  1.34s/it]
+ 22%|       | 108/500 [02:20<08:55,  1.36s/it]
+ 22%|       | 109/500 [02:21<08:51,  1.36s/it]
+ 22%|       | 110/500 [02:22<07:40,  1.18s/it]
+ 22%|       | 111/500 [02:24<07:56,  1.22s/it]
+ 22%|       | 112/500 [02:25<08:04,  1.25s/it]
+ 23%|       | 113/500 [02:26<08:13,  1.28s/it]
+ 23%|       | 114/500 [02:28<08:29,  1.32s/it]
+ 23%|       | 115/500 [02:29<08:32,  1.33s/it]
+ 23%|       | 116/500 [02:30<08:29,  1.33s/it]
+ 23%|       | 117/500 [02:32<08:41,  1.36s/it]
+ 24%|       | 118/500 [02:33<08:39,  1.36s/it]
+ 24%|       | 119/500 [02:34<08:35,  1.35s/it]
+ 24%|       | 120/500 [02:35<07:26,  1.18s/it]
+ 24%|       | 121/500 [02:37<07:42,  1.22s/it]
+ 24%|       | 122/500 [02:38<07:56,  1.26s/it]
+ 25%|       | 123/500 [02:39<08:03,  1.28s/it]
+ 25%|       | 124/500 [02:41<08:11,  1.31s/it]
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+ 25%|       | 126/500 [02:43<08:11,  1.31s/it]
+ 25%|       | 127/500 [02:45<08:11,  1.32s/it]
+ 26%|       | 128/500 [02:46<08:16,  1.34s/it]
+ 26%|       | 129/500 [02:47<08:17,  1.34s/it]
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+ 27%|       | 133/500 [02:52<07:49,  1.28s/it]
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+ 27%|       | 135/500 [02:55<08:00,  1.32s/it]
+ 27%|       | 136/500 [02:56<08:01,  1.32s/it]
+ 27%|       | 137/500 [02:57<07:57,  1.32s/it]
+ 28%|       | 138/500 [02:59<08:02,  1.33s/it]
+ 28%|       | 139/500 [03:00<08:03,  1.34s/it]
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+ 29%|       | 143/500 [03:05<07:40,  1.29s/it]
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+ 29%|       | 146/500 [03:09<07:49,  1.33s/it]
+ 29%|       | 147/500 [03:10<07:49,  1.33s/it]
+ 30%|       | 148/500 [03:12<07:47,  1.33s/it]
+ 30%|       | 149/500 [03:13<07:45,  1.33s/it]
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+ 31%|       | 153/500 [03:18<07:29,  1.29s/it]
+ 31%|       | 154/500 [03:19<07:30,  1.30s/it]
+ 31%|       | 155/500 [03:20<07:30,  1.31s/it]
+ 31%|       | 156/500 [03:22<07:34,  1.32s/it]
+ 31%|      | 157/500 [03:23<07:37,  1.34s/it]
+ 32%|      | 158/500 [03:25<07:34,  1.33s/it]
+ 32%|      | 159/500 [03:26<07:32,  1.33s/it]
+ 32%|      | 160/500 [03:27<06:34,  1.16s/it]
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+ 33%|      | 163/500 [03:31<07:13,  1.29s/it]
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+ 99%|| 493/500 [10:39<00:08,  1.28s/it]
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+ 99%|| 495/500 [10:42<00:06,  1.35s/it]
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+ 99%|| 497/500 [10:44<00:04,  1.34s/it]
+100%|| 498/500 [10:46<00:02,  1.35s/it]
+100%|| 499/500 [10:47<00:01,  1.37s/it]
+100%|| 500/500 [10:48<00:00,  1.19s/it]
+                                                 
+
+100%|| 500/500 [10:48<00:00,  1.19s/it]
+                                                 
+
+100%|| 500/500 [10:49<00:00,  1.19s/it]
+100%|| 500/500 [10:50<00:00,  1.30s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 650.0078, 'train_samples_per_second': 11.538, 'train_steps_per_second': 0.769, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 67%|   | 100/150 [00:30<00:15,  3.25it/s]
+200it [01:01,  3.25it/s]                         
+200it [01:01,  3.25it/s]
+
+  0%|          | 0/150 [00:00<?, ?it/s]
+ 66%|   | 99/150 [00:30<00:15,  3.22it/s]
+198it [01:01,  3.22it/s]                        
+198it [01:01,  3.22it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.25it/s]             
+100it [00:30,  3.25it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.27it/s]             
+100it [00:30,  3.27it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.23it/s]             
+100it [00:30,  3.23it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.23it/s]             
+100it [00:30,  3.23it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+98it [00:30,  3.19it/s]              
+98it [00:30,  3.19it/s]
+PASSED
+tests/plugins/generic/test_great.py::test_plugin_generate[False-test_plugin2] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<11:29,  1.38s/it]
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+ 31%|       | 155/500 [03:17<07:36,  1.32s/it]
+ 31%|       | 156/500 [03:18<07:34,  1.32s/it]
+ 31%|      | 157/500 [03:20<07:33,  1.32s/it]
+ 32%|      | 158/500 [03:21<07:34,  1.33s/it]
+ 32%|      | 159/500 [03:23<07:35,  1.34s/it]
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+ 32%|      | 161/500 [03:25<06:49,  1.21s/it]
+ 32%|      | 162/500 [03:26<06:59,  1.24s/it]
+ 33%|      | 163/500 [03:27<07:03,  1.26s/it]
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+                                                 
+
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+                                                 
+
+100%|| 500/500 [10:38<00:00,  1.16s/it]
+100%|| 500/500 [10:38<00:00,  1.28s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 638.1142, 'train_samples_per_second': 11.753, 'train_steps_per_second': 0.784, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/150 [00:00<?, ?it/s]
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+200it [01:01,  3.27it/s]                         
+200it [01:01,  3.27it/s]
+
+  0%|          | 0/150 [00:00<?, ?it/s]
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+198it [01:01,  3.22it/s]                        
+198it [01:01,  3.22it/s]
+
+  0%|          | 0/5 [00:00<?, ?it/s]
+100it [00:30,  3.26it/s]             
+100it [00:30,  3.26it/s]
+
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+
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+
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+
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+
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+100it [00:30,  3.26it/s]             
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+
+  0%|          | 0/5 [00:00<?, ?it/s]
+98it [00:30,  3.19it/s]              
+98it [00:30,  3.19it/s]
+PASSED
+tests/plugins/generic/test_great.py::test_sample_hyperparams PASSED
+tests/plugins/generic/test_great.py::test_eval_performance_great[True] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
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+100%|| 498/500 [10:46<00:02,  1.33s/it]
+100%|| 499/500 [10:47<00:01,  1.33s/it]
+100%|| 500/500 [10:48<00:00,  1.15s/it]
+                                                 
+
+100%|| 500/500 [10:48<00:00,  1.15s/it]
+                                                 
+
+100%|| 500/500 [10:49<00:00,  1.15s/it]
+100%|| 500/500 [10:49<00:00,  1.30s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 649.7326, 'train_samples_per_second': 11.543, 'train_steps_per_second': 0.77, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/100 [00:00<?, ?it/s]
+100%|| 100/100 [00:30<00:00,  3.24it/s]
+100%|| 100/100 [00:30<00:00,  3.24it/s]
+
+  0%|          | 0/100 [00:00<?, ?it/s]
+100%|| 100/100 [00:30<00:00,  3.26it/s]
+100%|| 100/100 [00:30<00:00,  3.26it/s]
+
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<11:48,  1.42s/it]
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+ 96%|| 482/500 [10:26<00:22,  1.27s/it]
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+ 98%|| 488/500 [10:35<00:16,  1.35s/it]
+ 98%|| 489/500 [10:36<00:14,  1.36s/it]
+ 98%|| 490/500 [10:37<00:11,  1.17s/it]
+ 98%|| 491/500 [10:38<00:11,  1.23s/it]
+ 98%|| 492/500 [10:40<00:10,  1.28s/it]
+ 99%|| 493/500 [10:41<00:09,  1.30s/it]
+ 99%|| 494/500 [10:42<00:07,  1.31s/it]
+ 99%|| 495/500 [10:44<00:06,  1.32s/it]
+ 99%|| 496/500 [10:45<00:05,  1.33s/it]
+ 99%|| 497/500 [10:46<00:04,  1.35s/it]
+100%|| 498/500 [10:48<00:02,  1.34s/it]
+100%|| 499/500 [10:49<00:01,  1.35s/it]
+100%|| 500/500 [10:50<00:00,  1.17s/it]
+                                                 
+
+100%|| 500/500 [10:50<00:00,  1.17s/it]
+                                                 
+
+100%|| 500/500 [10:51<00:00,  1.17s/it]
+100%|| 500/500 [10:51<00:00,  1.30s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 651.7748, 'train_samples_per_second': 11.507, 'train_steps_per_second': 0.767, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/100 [00:00<?, ?it/s]
+100%|| 100/100 [00:30<00:00,  3.28it/s]
+100%|| 100/100 [00:30<00:00,  3.28it/s]
+
+  0%|          | 0/100 [00:00<?, ?it/s]
+100%|| 100/100 [00:30<00:00,  3.28it/s]
+100%|| 100/100 [00:30<00:00,  3.28it/s]
+PASSED
+tests/plugins/generic/test_great.py::test_eval_performance_great[False] 
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<11:14,  1.35s/it]
+  0%|          | 2/500 [00:02<11:02,  1.33s/it]
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+  2%|         | 8/500 [00:10<11:16,  1.37s/it]
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+ 10%|         | 51/500 [01:06<09:19,  1.25s/it]
+ 10%|         | 52/500 [01:07<09:38,  1.29s/it]
+ 11%|         | 53/500 [01:09<09:46,  1.31s/it]
+ 11%|         | 54/500 [01:10<09:49,  1.32s/it]
+ 11%|         | 55/500 [01:12<09:54,  1.34s/it]
+ 11%|         | 56/500 [01:13<09:57,  1.35s/it]
+ 11%|        | 57/500 [01:14<09:54,  1.34s/it]
+ 12%|        | 58/500 [01:16<09:53,  1.34s/it]
+ 12%|        | 59/500 [01:17<10:00,  1.36s/it]
+ 12%|        | 60/500 [01:18<08:40,  1.18s/it]
+ 12%|        | 61/500 [01:19<08:57,  1.22s/it]
+ 12%|        | 62/500 [01:20<09:14,  1.26s/it]
+ 13%|        | 63/500 [01:22<09:21,  1.28s/it]
+ 13%|        | 64/500 [01:23<09:25,  1.30s/it]
+ 13%|        | 65/500 [01:24<09:34,  1.32s/it]
+ 13%|        | 66/500 [01:26<09:42,  1.34s/it]
+ 13%|        | 67/500 [01:27<09:37,  1.33s/it]
+ 14%|        | 68/500 [01:29<09:43,  1.35s/it]
+ 14%|        | 69/500 [01:30<09:44,  1.36s/it]
+ 14%|        | 70/500 [01:31<08:27,  1.18s/it]
+ 14%|        | 71/500 [01:32<08:48,  1.23s/it]
+ 14%|        | 72/500 [01:33<09:02,  1.27s/it]
+ 15%|        | 73/500 [01:35<09:15,  1.30s/it]
+ 15%|        | 74/500 [01:36<09:26,  1.33s/it]
+ 15%|        | 75/500 [01:37<09:23,  1.33s/it]
+ 15%|        | 76/500 [01:39<09:29,  1.34s/it]
+ 15%|        | 77/500 [01:40<09:28,  1.34s/it]
+ 16%|        | 78/500 [01:42<09:32,  1.36s/it]
+ 16%|        | 79/500 [01:43<09:26,  1.35s/it]
+ 16%|        | 80/500 [01:44<08:15,  1.18s/it]
+ 16%|        | 81/500 [01:45<08:32,  1.22s/it]
+ 16%|        | 82/500 [01:46<08:49,  1.27s/it]
+ 17%|        | 83/500 [01:48<09:02,  1.30s/it]
+ 17%|        | 84/500 [01:49<09:09,  1.32s/it]
+ 17%|        | 85/500 [01:51<09:16,  1.34s/it]
+ 17%|        | 86/500 [01:52<09:19,  1.35s/it]
+ 17%|        | 87/500 [01:53<09:18,  1.35s/it]
+ 18%|        | 88/500 [01:55<09:17,  1.35s/it]
+ 18%|        | 89/500 [01:56<09:11,  1.34s/it]
+ 18%|        | 90/500 [01:57<07:55,  1.16s/it]
+ 18%|        | 91/500 [01:58<08:14,  1.21s/it]
+ 18%|        | 92/500 [01:59<08:30,  1.25s/it]
+ 19%|        | 93/500 [02:01<08:39,  1.28s/it]
+ 19%|        | 94/500 [02:02<08:49,  1.30s/it]
+ 19%|        | 95/500 [02:03<08:55,  1.32s/it]
+ 19%|        | 96/500 [02:05<08:54,  1.32s/it]
+ 19%|        | 97/500 [02:06<08:53,  1.32s/it]
+ 20%|        | 98/500 [02:07<08:54,  1.33s/it]
+ 20%|        | 99/500 [02:09<08:55,  1.33s/it]
+ 20%|        | 100/500 [02:10<07:46,  1.17s/it]
+ 20%|        | 101/500 [02:11<08:10,  1.23s/it]
+ 20%|        | 102/500 [02:12<08:25,  1.27s/it]
+ 21%|        | 103/500 [02:14<08:31,  1.29s/it]
+ 21%|        | 104/500 [02:15<08:37,  1.31s/it]
+ 21%|        | 105/500 [02:16<08:38,  1.31s/it]
+ 21%|        | 106/500 [02:18<08:38,  1.32s/it]
+ 21%|       | 107/500 [02:19<08:36,  1.31s/it]
+ 22%|       | 108/500 [02:20<08:38,  1.32s/it]
+ 22%|       | 109/500 [02:22<08:39,  1.33s/it]
+ 22%|       | 110/500 [02:22<07:29,  1.15s/it]
+ 22%|       | 111/500 [02:24<07:56,  1.22s/it]
+ 22%|       | 112/500 [02:25<08:11,  1.27s/it]
+ 23%|       | 113/500 [02:26<08:20,  1.29s/it]
+ 23%|       | 114/500 [02:28<08:37,  1.34s/it]
+ 23%|       | 115/500 [02:29<08:40,  1.35s/it]
+ 23%|       | 116/500 [02:31<08:40,  1.36s/it]
+ 23%|       | 117/500 [02:32<08:41,  1.36s/it]
+ 24%|       | 118/500 [02:33<08:34,  1.35s/it]
+ 24%|       | 119/500 [02:35<08:33,  1.35s/it]
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+ 25%|       | 123/500 [02:40<08:07,  1.29s/it]
+ 25%|       | 124/500 [02:41<08:18,  1.33s/it]
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+ 25%|       | 126/500 [02:44<08:24,  1.35s/it]
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+ 26%|       | 128/500 [02:46<08:26,  1.36s/it]
+ 26%|       | 129/500 [02:48<08:24,  1.36s/it]
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+ 27%|       | 133/500 [02:53<07:54,  1.29s/it]
+ 27%|       | 134/500 [02:54<08:01,  1.32s/it]
+ 27%|       | 135/500 [02:55<08:01,  1.32s/it]
+ 27%|       | 136/500 [02:57<08:06,  1.34s/it]
+ 27%|       | 137/500 [02:58<08:06,  1.34s/it]
+ 28%|       | 138/500 [02:59<08:08,  1.35s/it]
+ 28%|       | 139/500 [03:01<08:06,  1.35s/it]
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+ 28%|       | 141/500 [03:03<07:20,  1.23s/it]
+ 28%|       | 142/500 [03:04<07:31,  1.26s/it]
+ 29%|       | 143/500 [03:06<07:43,  1.30s/it]
+ 29%|       | 144/500 [03:07<07:46,  1.31s/it]
+ 29%|       | 145/500 [03:08<07:47,  1.32s/it]
+ 29%|       | 146/500 [03:10<07:52,  1.33s/it]
+ 29%|       | 147/500 [03:11<07:49,  1.33s/it]
+ 30%|       | 148/500 [03:12<07:51,  1.34s/it]
+ 30%|       | 149/500 [03:14<07:52,  1.34s/it]
+ 30%|       | 150/500 [03:14<06:49,  1.17s/it]
+ 30%|       | 151/500 [03:16<07:10,  1.23s/it]
+ 30%|       | 152/500 [03:17<07:22,  1.27s/it]
+ 31%|       | 153/500 [03:18<07:28,  1.29s/it]
+ 31%|       | 154/500 [03:20<07:34,  1.31s/it]
+ 31%|       | 155/500 [03:21<07:40,  1.34s/it]
+ 31%|       | 156/500 [03:23<07:39,  1.34s/it]
+ 31%|      | 157/500 [03:24<07:43,  1.35s/it]
+ 32%|      | 158/500 [03:25<07:43,  1.35s/it]
+ 32%|      | 159/500 [03:27<07:42,  1.36s/it]
+ 32%|      | 160/500 [03:27<06:40,  1.18s/it]
+ 32%|      | 161/500 [03:29<07:08,  1.26s/it]
+ 32%|      | 162/500 [03:30<07:20,  1.30s/it]
+ 33%|      | 163/500 [03:32<07:22,  1.31s/it]
+ 33%|      | 164/500 [03:33<07:26,  1.33s/it]
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+ 33%|      | 166/500 [03:36<07:25,  1.33s/it]
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+ 34%|      | 168/500 [03:38<07:31,  1.36s/it]
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+ 68%|   | 341/500 [07:24<03:18,  1.25s/it]
+ 68%|   | 342/500 [07:25<03:22,  1.28s/it]
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+ 91%|| 457/500 [09:54<00:57,  1.33s/it]
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+ 95%|| 476/500 [10:19<00:31,  1.32s/it]
+ 95%|| 477/500 [10:20<00:30,  1.35s/it]
+ 96%|| 478/500 [10:22<00:29,  1.35s/it]
+ 96%|| 479/500 [10:23<00:28,  1.35s/it]
+ 96%|| 480/500 [10:24<00:23,  1.17s/it]
+ 96%|| 481/500 [10:25<00:23,  1.22s/it]
+ 96%|| 482/500 [10:27<00:22,  1.28s/it]
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+ 97%|| 484/500 [10:29<00:21,  1.31s/it]
+ 97%|| 485/500 [10:31<00:20,  1.34s/it]
+ 97%|| 486/500 [10:32<00:18,  1.34s/it]
+ 97%|| 487/500 [10:33<00:17,  1.34s/it]
+ 98%|| 488/500 [10:35<00:16,  1.34s/it]
+ 98%|| 489/500 [10:36<00:14,  1.35s/it]
+ 98%|| 490/500 [10:37<00:11,  1.17s/it]
+ 98%|| 491/500 [10:38<00:11,  1.23s/it]
+ 98%|| 492/500 [10:40<00:10,  1.28s/it]
+ 99%|| 493/500 [10:41<00:09,  1.30s/it]
+ 99%|| 494/500 [10:42<00:07,  1.31s/it]
+ 99%|| 495/500 [10:44<00:06,  1.34s/it]
+ 99%|| 496/500 [10:45<00:05,  1.34s/it]
+ 99%|| 497/500 [10:46<00:04,  1.34s/it]
+100%|| 498/500 [10:48<00:02,  1.35s/it]
+100%|| 499/500 [10:49<00:01,  1.35s/it]
+100%|| 500/500 [10:50<00:00,  1.17s/it]
+                                                 
+
+100%|| 500/500 [10:50<00:00,  1.17s/it]
+                                                 
+
+100%|| 500/500 [10:51<00:00,  1.17s/it]
+100%|| 500/500 [10:51<00:00,  1.30s/it]
+{'loss': 0.4381, 'grad_norm': 3.715604305267334, 'learning_rate': 0.0, 'epoch': 50.0}
+{'train_runtime': 651.9299, 'train_samples_per_second': 11.504, 'train_steps_per_second': 0.767, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
+  0%|          | 0/100 [00:00<?, ?it/s]
+100%|| 100/100 [00:30<00:00,  3.28it/s]
+100%|| 100/100 [00:30<00:00,  3.28it/s]
+
+  0%|          | 0/100 [00:00<?, ?it/s]
+100%|| 100/100 [00:30<00:00,  3.26it/s]
+100%|| 100/100 [00:30<00:00,  3.26it/s]
+
+  0%|          | 0/500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
+  0%|          | 1/500 [00:01<11:20,  1.36s/it]
+  0%|          | 2/500 [00:02<11:10,  1.35s/it]
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+ 24%|       | 119/500 [02:35<08:44,  1.38s/it]
+ 24%|       | 120/500 [02:36<07:31,  1.19s/it]
+ 24%|       | 121/500 [02:37<07:54,  1.25s/it]
+ 24%|       | 122/500 [02:38<08:08,  1.29s/it]
+ 25%|       | 123/500 [02:40<08:15,  1.32s/it]
+ 25%|       | 124/500 [02:41<08:21,  1.33s/it]
+ 25%|       | 125/500 [02:42<08:22,  1.34s/it]
+ 25%|       | 126/500 [02:44<08:25,  1.35s/it]
+ 25%|       | 127/500 [02:45<08:28,  1.36s/it]
+ 26%|       | 128/500 [02:47<08:27,  1.36s/it]
+ 26%|       | 129/500 [02:48<08:23,  1.36s/it]
+ 26%|       | 130/500 [02:49<07:14,  1.17s/it]
+ 26%|       | 131/500 [02:50<07:28,  1.22s/it]
+ 26%|       | 132/500 [02:51<07:39,  1.25s/it]
+ 27%|       | 133/500 [02:53<07:48,  1.28s/it]
+ 27%|       | 134/500 [02:54<07:55,  1.30s/it]
+ 27%|       | 135/500 [02:55<08:04,  1.33s/it]
+ 27%|       | 136/500 [02:57<08:06,  1.34s/it]
+ 27%|       | 137/500 [02:58<08:04,  1.33s/it]
+ 28%|       | 138/500 [02:59<08:13,  1.36s/it]
+ 28%|       | 139/500 [03:01<08:13,  1.37s/it]
+ 28%|       | 140/500 [03:02<07:08,  1.19s/it]
+ 28%|       | 141/500 [03:03<07:30,  1.26s/it]
+ 28%|       | 142/500 [03:04<07:46,  1.30s/it]
+ 29%|       | 143/500 [03:06<07:50,  1.32s/it]
+ 29%|       | 144/500 [03:07<07:53,  1.33s/it]
+ 29%|       | 145/500 [03:09<07:59,  1.35s/it]
+ 29%|       | 146/500 [03:10<07:56,  1.35s/it]
+ 29%|       | 147/500 [03:11<07:51,  1.33s/it]
+ 30%|       | 148/500 [03:13<07:52,  1.34s/it]
+ 30%|       | 149/500 [03:14<07:51,  1.34s/it]
+ 30%|       | 150/500 [03:15<06:49,  1.17s/it]
+ 30%|       | 151/500 [03:16<07:08,  1.23s/it]
+ 30%|       | 152/500 [03:17<07:19,  1.26s/it]
+ 31%|       | 153/500 [03:19<07:28,  1.29s/it]
+ 31%|       | 154/500 [03:20<07:37,  1.32s/it]
+ 31%|       | 155/500 [03:21<07:36,  1.32s/it]
+ 31%|       | 156/500 [03:23<07:35,  1.33s/it]
+ 31%|      | 157/500 [03:24<07:36,  1.33s/it]
+ 32%|      | 158/500 [03:26<07:37,  1.34s/it]
+ 32%|      | 159/500 [03:27<07:44,  1.36s/it]
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+ 32%|      | 161/500 [03:29<06:58,  1.23s/it]
+ 32%|      | 162/500 [03:30<07:05,  1.26s/it]
+ 33%|      | 163/500 [03:32<07:13,  1.29s/it]
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+ 34%|      | 168/500 [03:39<07:29,  1.35s/it]
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+                                                 
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+                                                 
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+{'train_runtime': 651.8809, 'train_samples_per_second': 11.505, 'train_steps_per_second': 0.767, 'train_loss': 0.43807760620117187, 'epoch': 50.0}
+
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+
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+PASSED
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+PASSED
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+PASSED
+tests/plugins/generic/test_tvae.py::test_plugin_generate[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  4.93it/s]
+ 20%|        | 2/10 [00:00<00:01,  5.43it/s]
+ 30%|       | 3/10 [00:00<00:01,  5.65it/s]
+ 40%|      | 4/10 [00:00<00:01,  5.69it/s]
+ 50%|     | 5/10 [00:00<00:00,  5.76it/s]
+ 60%|    | 6/10 [00:01<00:00,  5.73it/s]
+ 70%|   | 7/10 [00:01<00:00,  5.82it/s]
+ 80%|  | 8/10 [00:01<00:00,  5.84it/s]
+ 90%| | 9/10 [00:01<00:00,  5.74it/s]
+100%|| 10/10 [00:01<00:00,  5.83it/s]
+100%|| 10/10 [00:01<00:00,  5.73it/s]
+PASSED
+tests/plugins/generic/test_tvae.py::test_plugin_generate[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:02,  4.44it/s]
+ 20%|        | 2/10 [00:00<00:01,  5.16it/s]
+ 30%|       | 3/10 [00:00<00:01,  5.49it/s]
+ 40%|      | 4/10 [00:00<00:01,  5.54it/s]
+ 50%|     | 5/10 [00:00<00:00,  5.70it/s]
+ 60%|    | 6/10 [00:01<00:00,  5.77it/s]
+ 70%|   | 7/10 [00:01<00:00,  5.90it/s]
+ 80%|  | 8/10 [00:01<00:00,  5.94it/s]
+ 90%| | 9/10 [00:01<00:00,  5.85it/s]
+100%|| 10/10 [00:01<00:00,  5.86it/s]
+100%|| 10/10 [00:01<00:00,  5.70it/s]
+PASSED
+tests/plugins/generic/test_tvae.py::test_plugin_generate[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  4.84it/s]
+ 20%|        | 2/10 [00:00<00:01,  5.39it/s]
+ 30%|       | 3/10 [00:00<00:01,  5.63it/s]
+ 40%|      | 4/10 [00:00<00:01,  5.70it/s]
+ 50%|     | 5/10 [00:00<00:00,  5.68it/s]
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+ 70%|   | 7/10 [00:01<00:00,  5.80it/s]
+ 80%|  | 8/10 [00:01<00:00,  5.79it/s]
+ 90%| | 9/10 [00:01<00:00,  5.64it/s]
+100%|| 10/10 [00:01<00:00,  5.71it/s]
+100%|| 10/10 [00:01<00:00,  5.66it/s]
+PASSED
+tests/plugins/generic/test_tvae.py::test_plugin_generate_constraints[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 40%|      | 4/10 [00:00<00:00, 35.89it/s]
+100%|| 10/10 [00:00<00:00, 47.29it/s]
+100%|| 10/10 [00:00<00:00, 45.50it/s]
+PASSED
+tests/plugins/generic/test_tvae.py::test_plugin_generate_constraints[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 40%|      | 4/10 [00:00<00:00, 37.52it/s]
+100%|| 10/10 [00:00<00:00, 49.19it/s]
+100%|| 10/10 [00:00<00:00, 47.36it/s]
+PASSED
+tests/plugins/generic/test_tvae.py::test_plugin_generate_constraints[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 40%|      | 4/10 [00:00<00:00, 34.24it/s]
+100%|| 10/10 [00:00<00:00, 46.07it/s]
+100%|| 10/10 [00:00<00:00, 44.18it/s]
+PASSED
+tests/plugins/generic/test_tvae.py::test_sample_hyperparams PASSED
+tests/plugins/generic/test_tvae.py::test_plugin_conditional 
+  0%|          | 0/1000 [00:00<?, ?it/s]
+  0%|          | 2/1000 [00:00<01:10, 14.20it/s]
+  0%|          | 4/1000 [00:00<00:58, 17.03it/s]
+  1%|          | 6/1000 [00:00<00:54, 18.24it/s]
+  1%|          | 8/1000 [00:00<00:52, 18.78it/s]
+  1%|          | 11/1000 [00:00<00:50, 19.47it/s]
+  1%|         | 13/1000 [00:00<00:50, 19.56it/s]
+  2%|         | 16/1000 [00:00<00:49, 19.82it/s]
+  2%|         | 18/1000 [00:00<00:49, 19.84it/s]
+  2%|         | 20/1000 [00:01<00:49, 19.75it/s]
+  2%|         | 23/1000 [00:01<00:49, 19.92it/s]
+  2%|         | 25/1000 [00:01<00:49, 19.76it/s]
+  3%|         | 27/1000 [00:01<00:49, 19.68it/s]
+  3%|         | 30/1000 [00:01<00:48, 19.84it/s]
+  3%|         | 32/1000 [00:01<00:48, 19.81it/s]
+  4%|         | 35/1000 [00:01<00:48, 19.83it/s]
+  4%|         | 37/1000 [00:01<00:48, 19.74it/s]
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+  4%|         | 45/1000 [00:02<00:47, 19.99it/s]
+  5%|         | 48/1000 [00:02<00:47, 20.09it/s]
+  5%|         | 51/1000 [00:02<00:48, 19.67it/s]
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+  6%|         | 59/1000 [00:03<00:47, 19.89it/s]
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+  6%|         | 64/1000 [00:03<00:47, 19.78it/s]
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+  7%|         | 68/1000 [00:03<00:47, 19.67it/s]
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+  8%|         | 79/1000 [00:04<00:48, 19.16it/s]
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+  8%|         | 85/1000 [00:04<00:47, 19.18it/s]
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+  9%|         | 89/1000 [00:04<00:47, 19.20it/s]
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+ 12%|        | 116/1000 [00:05<00:46, 19.00it/s]
+ 12%|        | 118/1000 [00:06<00:46, 19.07it/s]
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+ 13%|        | 132/1000 [00:06<00:44, 19.31it/s]
+ 13%|        | 134/1000 [00:06<00:44, 19.32it/s]
+ 14%|        | 136/1000 [00:07<00:45, 19.10it/s]
+ 14%|        | 138/1000 [00:07<00:46, 18.59it/s]
+ 14%|        | 140/1000 [00:07<00:45, 18.76it/s]
+ 14%|        | 142/1000 [00:07<00:45, 18.74it/s]
+ 14%|        | 144/1000 [00:07<00:45, 18.66it/s]
+ 15%|        | 146/1000 [00:07<00:44, 19.01it/s]
+ 15%|        | 148/1000 [00:07<00:44, 19.15it/s]
+ 15%|        | 151/1000 [00:07<00:44, 19.03it/s]
+ 15%|        | 153/1000 [00:07<00:44, 18.87it/s]
+ 16%|        | 155/1000 [00:08<00:44, 19.03it/s]
+ 16%|        | 157/1000 [00:08<00:43, 19.22it/s]
+ 16%|        | 159/1000 [00:08<00:44, 19.11it/s]
+ 16%|        | 161/1000 [00:08<00:44, 19.06it/s]
+ 16%|        | 163/1000 [00:08<00:43, 19.08it/s]
+ 16%|        | 165/1000 [00:08<00:43, 19.15it/s]
+ 17%|        | 167/1000 [00:08<00:43, 19.24it/s]
+ 17%|        | 169/1000 [00:08<00:44, 18.56it/s]
+ 17%|        | 171/1000 [00:08<00:44, 18.73it/s]
+ 17%|        | 173/1000 [00:08<00:44, 18.64it/s]
+ 18%|        | 175/1000 [00:09<00:44, 18.73it/s]
+ 18%|        | 177/1000 [00:09<00:44, 18.69it/s]
+ 18%|        | 179/1000 [00:09<00:44, 18.66it/s]
+ 18%|        | 181/1000 [00:09<00:43, 18.78it/s]
+ 18%|        | 183/1000 [00:09<00:43, 18.92it/s]
+ 18%|        | 185/1000 [00:09<00:43, 18.93it/s]
+ 19%|        | 187/1000 [00:09<00:42, 18.96it/s]
+ 19%|        | 189/1000 [00:09<00:42, 19.02it/s]
+ 19%|        | 191/1000 [00:09<00:42, 19.07it/s]
+ 19%|        | 193/1000 [00:10<00:42, 18.91it/s]
+ 20%|        | 195/1000 [00:10<00:43, 18.60it/s]
+ 20%|        | 197/1000 [00:10<00:42, 18.95it/s]
+ 20%|        | 199/1000 [00:10<00:42, 18.76it/s]
+ 20%|        | 201/1000 [00:10<00:43, 18.46it/s]
+ 20%|        | 203/1000 [00:10<00:42, 18.55it/s]
+ 20%|        | 205/1000 [00:10<00:43, 18.47it/s]
+ 21%|        | 207/1000 [00:10<00:42, 18.77it/s]
+ 21%|        | 209/1000 [00:10<00:42, 18.76it/s]
+ 21%|        | 211/1000 [00:11<00:41, 19.01it/s]
+ 21%|       | 213/1000 [00:11<00:41, 19.12it/s]
+ 22%|       | 215/1000 [00:11<00:41, 18.72it/s]
+ 22%|       | 217/1000 [00:11<00:41, 18.83it/s]
+ 22%|       | 219/1000 [00:11<00:41, 18.80it/s]
+ 22%|       | 221/1000 [00:11<00:41, 18.65it/s]
+ 22%|       | 223/1000 [00:11<00:41, 18.59it/s]
+ 22%|       | 225/1000 [00:11<00:41, 18.85it/s]
+ 23%|       | 227/1000 [00:11<00:40, 18.87it/s]
+ 23%|       | 229/1000 [00:11<00:40, 18.90it/s]
+ 23%|       | 231/1000 [00:12<00:40, 18.96it/s]
+ 23%|       | 233/1000 [00:12<00:40, 18.91it/s]
+ 24%|       | 235/1000 [00:12<00:40, 18.84it/s]
+ 24%|       | 237/1000 [00:12<00:40, 18.96it/s]
+ 24%|       | 239/1000 [00:12<00:40, 19.01it/s]
+ 24%|       | 241/1000 [00:12<00:39, 18.99it/s]
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+ 25%|       | 246/1000 [00:12<00:39, 18.99it/s]
+ 25%|       | 248/1000 [00:12<00:39, 18.94it/s]
+ 25%|       | 250/1000 [00:13<00:39, 18.94it/s]
+ 25%|       | 252/1000 [00:13<00:40, 18.45it/s]
+ 25%|       | 254/1000 [00:13<00:40, 18.56it/s]
+ 26%|       | 256/1000 [00:13<00:39, 18.87it/s]
+ 26%|       | 258/1000 [00:13<00:39, 18.81it/s]
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+ 29%|       | 286/1000 [00:14<00:37, 18.79it/s]
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+ 50%|     | 498/1000 [00:26<00:26, 18.88it/s]
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+100%|| 1000/1000 [00:54<00:00, 17.57it/s]
+100%|| 1000/1000 [00:54<00:00, 18.51it/s]
+PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_fit[test_plugin0] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_fit[test_plugin1] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_fit[test_plugin2] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_generate[test_plugin0] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_generate[test_plugin1] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_generate[test_plugin2] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_generate_constraints[test_plugin0] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_generate_constraints[test_plugin1] PASSED
+tests/plugins/generic/test_uniform_sampler.py::test_plugin_generate_constraints[test_plugin2] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_fit 
+  0%|          | 0/5 [00:00<?, ?it/s]
+ 20%|        | 1/5 [00:08<00:35,  8.88s/it]
+ 40%|      | 2/5 [00:17<00:26,  8.82s/it]
+ 60%|    | 3/5 [00:26<00:17,  8.81s/it]
+ 80%|  | 4/5 [00:35<00:08,  8.80s/it]
+100%|| 5/5 [00:44<00:00,  8.85s/it]
+100%|| 5/5 [00:44<00:00,  8.83s/it]
+PASSED
+tests/plugins/images/test_image_adsgan.py::test_plugin_generate 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:08<01:18,  8.67s/it]
+ 20%|        | 2/10 [00:17<01:09,  8.66s/it]
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+100%|| 10/10 [01:27<00:00,  8.82s/it]
+100%|| 10/10 [01:27<00:00,  8.76s/it]
+PASSED
+tests/plugins/images/test_image_adsgan.py::test_sample_hyperparams PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_generate 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:08<01:19,  8.84s/it]
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+100%|| 10/10 [01:27<00:00,  8.70s/it]
+100%|| 10/10 [01:27<00:00,  8.72s/it]
+PASSED
+tests/plugins/images/test_image_cgan.py::test_plugin_generate_with_conditional 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:08<01:18,  8.68s/it]
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+100%|| 10/10 [01:27<00:00,  8.74s/it]
+100%|| 10/10 [01:27<00:00,  8.72s/it]
+PASSED
+tests/plugins/images/test_image_cgan.py::test_sample_hyperparams PASSED
+tests/plugins/privacy/test_adsgan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/privacy/test_adsgan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/privacy/test_adsgan.py::test_plugin_sanity[test_plugin2] PASSED
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+tests/plugins/privacy/test_adsgan.py::test_plugin_fit 
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+ 93%|| 93/100 [00:03<00:00, 33.23it/s]
+ 97%|| 97/100 [00:03<00:00, 33.78it/s]
+100%|| 100/100 [00:04<00:00, 21.90it/s]
+PASSED
+tests/plugins/privacy/test_adsgan.py::test_plugin_generate 
+  0%|          | 0/100 [00:00<?, ?it/s]
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+ 92%|| 92/100 [00:27<00:02,  3.71it/s]
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+100%|| 100/100 [00:31<00:00,  1.12it/s]
+100%|| 100/100 [00:31<00:00,  3.17it/s]
+PASSED
+tests/plugins/privacy/test_adsgan.py::test_plugin_conditional_adsgan 
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+  0%|          | 3/10000 [00:00<06:56, 23.98it/s]
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+  4%|         | 376/10000 [00:18<06:22, 25.19it/s]
+  4%|         | 380/10000 [00:18<06:01, 26.61it/s]
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+  6%|         | 646/10000 [00:31<05:18, 29.36it/s]
+  6%|         | 649/10000 [00:31<07:40, 20.31it/s]
+PASSED
+tests/plugins/privacy/test_adsgan.py::test_plugin_generate_constraints 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  3%|         | 3/100 [00:00<00:03, 25.51it/s]
+  7%|         | 7/100 [00:00<00:03, 30.93it/s]
+ 11%|         | 11/100 [00:00<00:02, 32.18it/s]
+ 15%|        | 15/100 [00:00<00:02, 32.76it/s]
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+ 27%|       | 27/100 [00:00<00:02, 33.87it/s]
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+ 87%| | 87/100 [00:02<00:00, 34.58it/s]
+ 91%| | 91/100 [00:02<00:00, 33.81it/s]
+ 95%|| 95/100 [00:02<00:00, 34.67it/s]
+ 99%|| 99/100 [00:02<00:00, 34.98it/s]
+100%|| 100/100 [00:03<00:00, 26.66it/s]
+PASSED
+tests/plugins/privacy/test_adsgan.py::test_sample_hyperparams PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_fit[test_plugin0] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_fit[test_plugin1] PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_fit[test_plugin2] PASSED
+tests/plugins/privacy/test_aim.py::test_sample_hyperparams PASSED
+tests/plugins/privacy/test_aim.py::test_plugin_encoding PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin0] 
+  0%|          | 0/1000 [00:00<?, ?it/s]
+  0%|          | 2/1000 [00:00<01:00, 16.54it/s]
+  0%|          | 5/1000 [00:00<00:50, 19.73it/s]
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+  1%|          | 11/1000 [00:00<00:47, 20.74it/s]
+  1%|         | 14/1000 [00:00<00:46, 21.23it/s]
+  2%|         | 17/1000 [00:00<00:45, 21.48it/s]
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+  2%|         | 23/1000 [00:01<00:44, 21.80it/s]
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+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+FAILED
+tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin1] 
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+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+FAILED
+tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin2] 
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+ 15%|        | 149/1000 [00:06<00:40, 21.23it/s]
+ 15%|        | 152/1000 [00:07<00:39, 21.25it/s]
+ 16%|        | 155/1000 [00:07<00:40, 21.12it/s]
+ 16%|        | 158/1000 [00:07<00:40, 21.03it/s]
+ 16%|        | 161/1000 [00:07<00:40, 20.91it/s]
+ 16%|        | 164/1000 [00:07<00:39, 21.16it/s]
+ 17%|        | 167/1000 [00:07<00:38, 21.46it/s]
+ 17%|        | 170/1000 [00:07<00:38, 21.39it/s]
+ 17%|        | 173/1000 [00:08<00:39, 20.90it/s]
+ 18%|        | 176/1000 [00:08<00:40, 20.42it/s]
+ 18%|        | 179/1000 [00:08<00:40, 20.09it/s]
+ 18%|        | 182/1000 [00:08<00:40, 20.21it/s]
+ 18%|        | 185/1000 [00:08<00:39, 20.38it/s]
+ 19%|        | 188/1000 [00:08<00:39, 20.62it/s]
+ 19%|        | 191/1000 [00:09<00:39, 20.64it/s]
+ 19%|        | 194/1000 [00:09<00:39, 20.59it/s]
+ 20%|        | 197/1000 [00:09<00:38, 20.67it/s]
+ 20%|        | 200/1000 [00:09<00:38, 20.93it/s]
+ 20%|        | 203/1000 [00:09<00:37, 21.12it/s]
+ 21%|        | 206/1000 [00:09<00:37, 21.07it/s]
+ 21%|        | 209/1000 [00:09<00:37, 21.08it/s]
+ 21%|        | 212/1000 [00:10<00:37, 21.27it/s]
+ 22%|       | 215/1000 [00:10<00:37, 21.04it/s]
+ 22%|       | 218/1000 [00:10<00:37, 20.94it/s]
+ 22%|       | 221/1000 [00:10<00:37, 20.83it/s]
+ 22%|       | 224/1000 [00:10<00:37, 20.46it/s]
+ 23%|       | 227/1000 [00:10<00:38, 20.18it/s]
+ 23%|       | 230/1000 [00:10<00:37, 20.55it/s]
+ 23%|       | 233/1000 [00:11<00:37, 20.73it/s]
+ 24%|       | 236/1000 [00:11<00:36, 21.06it/s]
+ 24%|       | 239/1000 [00:11<00:35, 21.20it/s]
+ 24%|       | 242/1000 [00:11<00:36, 20.93it/s]
+ 24%|       | 245/1000 [00:11<00:36, 20.87it/s]
+ 25%|       | 248/1000 [00:11<00:37, 20.26it/s]
+ 25%|       | 251/1000 [00:11<00:37, 20.18it/s]
+ 25%|       | 254/1000 [00:12<00:36, 20.18it/s]
+ 26%|       | 257/1000 [00:12<00:35, 20.65it/s]
+ 26%|       | 260/1000 [00:12<00:36, 20.33it/s]
+ 26%|       | 263/1000 [00:12<00:36, 20.38it/s]
+ 27%|       | 266/1000 [00:12<00:35, 20.54it/s]
+ 27%|       | 269/1000 [00:12<00:35, 20.49it/s]
+ 27%|       | 272/1000 [00:12<00:35, 20.57it/s]
+ 28%|       | 275/1000 [00:13<00:34, 20.88it/s]
+ 28%|       | 278/1000 [00:13<00:34, 21.14it/s]
+ 28%|       | 281/1000 [00:13<00:33, 21.19it/s]
+ 28%|       | 284/1000 [00:13<00:33, 21.09it/s]
+ 29%|       | 287/1000 [00:13<00:33, 21.10it/s]
+ 29%|       | 290/1000 [00:13<00:33, 20.99it/s]
+ 29%|       | 293/1000 [00:13<00:33, 21.25it/s]
+ 30%|       | 296/1000 [00:14<00:33, 21.15it/s]
+ 30%|       | 299/1000 [00:14<00:33, 21.12it/s]
+ 30%|       | 302/1000 [00:14<00:33, 21.08it/s]
+ 30%|       | 305/1000 [00:14<00:33, 20.81it/s]
+ 31%|       | 308/1000 [00:14<00:33, 20.69it/s]
+ 31%|       | 311/1000 [00:14<00:32, 21.06it/s]
+ 31%|      | 314/1000 [00:14<00:32, 21.34it/s]
+ 32%|      | 317/1000 [00:15<00:31, 21.36it/s]
+ 32%|      | 320/1000 [00:15<00:31, 21.30it/s]
+ 32%|      | 323/1000 [00:15<00:32, 20.93it/s]
+ 33%|      | 326/1000 [00:15<00:32, 20.73it/s]
+ 33%|      | 329/1000 [00:15<00:32, 20.37it/s]
+ 33%|      | 332/1000 [00:15<00:32, 20.38it/s]
+ 34%|      | 335/1000 [00:15<00:32, 20.57it/s]
+ 34%|      | 338/1000 [00:16<00:31, 20.75it/s]
+ 34%|      | 341/1000 [00:16<00:31, 20.63it/s]
+ 34%|      | 344/1000 [00:16<00:32, 20.40it/s]
+ 35%|      | 347/1000 [00:16<00:32, 20.29it/s]
+ 35%|      | 350/1000 [00:16<00:32, 20.26it/s]
+ 35%|      | 353/1000 [00:16<00:31, 20.39it/s]
+ 36%|      | 356/1000 [00:16<00:31, 20.60it/s]
+ 36%|      | 359/1000 [00:17<00:30, 20.80it/s]
+ 36%|      | 362/1000 [00:17<00:30, 20.88it/s]
+ 36%|      | 365/1000 [00:17<00:30, 20.89it/s]
+ 37%|      | 368/1000 [00:17<00:30, 20.68it/s]
+ 37%|      | 371/1000 [00:17<00:30, 20.67it/s]
+ 37%|      | 374/1000 [00:17<00:31, 20.16it/s]
+ 38%|      | 377/1000 [00:17<00:31, 19.94it/s]
+ 38%|      | 380/1000 [00:18<00:30, 20.19it/s]
+ 38%|      | 383/1000 [00:18<00:30, 20.39it/s]
+ 39%|      | 386/1000 [00:18<00:29, 20.57it/s]
+ 39%|      | 389/1000 [00:18<00:29, 20.62it/s]
+ 39%|      | 392/1000 [00:18<00:29, 20.51it/s]
+ 40%|      | 395/1000 [00:18<00:29, 20.21it/s]
+ 40%|      | 398/1000 [00:19<00:29, 20.14it/s]
+ 40%|      | 401/1000 [00:19<00:30, 19.95it/s]
+ 40%|      | 403/1000 [00:19<00:29, 19.93it/s]
+ 41%|      | 406/1000 [00:19<00:29, 20.42it/s]
+ 41%|      | 409/1000 [00:19<00:28, 20.71it/s]
+ 41%|      | 412/1000 [00:19<00:27, 21.05it/s]
+ 42%|     | 415/1000 [00:19<00:27, 21.08it/s]
+ 42%|     | 418/1000 [00:19<00:27, 20.97it/s]
+ 42%|     | 421/1000 [00:20<00:27, 20.94it/s]
+ 42%|     | 424/1000 [00:20<00:27, 20.82it/s]
+ 43%|     | 427/1000 [00:20<00:27, 20.72it/s]
+ 43%|     | 430/1000 [00:20<00:27, 20.60it/s]
+ 43%|     | 433/1000 [00:20<00:27, 20.73it/s]
+ 44%|     | 436/1000 [00:20<00:27, 20.87it/s]
+ 44%|     | 439/1000 [00:20<00:26, 20.80it/s]
+ 44%|     | 442/1000 [00:21<00:27, 20.51it/s]
+ 44%|     | 445/1000 [00:21<00:27, 20.42it/s]
+ 45%|     | 448/1000 [00:21<00:27, 20.17it/s]
+ 45%|     | 451/1000 [00:21<00:27, 19.87it/s]
+ 45%|     | 454/1000 [00:21<00:27, 20.04it/s]
+ 46%|     | 457/1000 [00:21<00:27, 19.99it/s]
+ 46%|     | 460/1000 [00:22<00:27, 19.61it/s]
+ 46%|     | 462/1000 [00:22<00:27, 19.51it/s]
+ 46%|     | 465/1000 [00:22<00:27, 19.75it/s]
+ 47%|     | 468/1000 [00:22<00:26, 19.96it/s]
+ 47%|     | 471/1000 [00:22<00:26, 20.28it/s]
+ 47%|     | 474/1000 [00:22<00:25, 20.49it/s]
+ 48%|     | 477/1000 [00:22<00:25, 20.35it/s]
+ 48%|     | 480/1000 [00:23<00:25, 20.40it/s]
+ 48%|     | 483/1000 [00:23<00:25, 20.41it/s]
+ 49%|     | 486/1000 [00:23<00:25, 20.28it/s]
+ 49%|     | 489/1000 [00:23<00:25, 20.36it/s]
+ 49%|     | 492/1000 [00:23<00:25, 20.29it/s]
+ 50%|     | 495/1000 [00:23<00:24, 20.36it/s]
+ 50%|     | 498/1000 [00:23<00:24, 20.50it/s]
+ 50%|     | 501/1000 [00:24<00:24, 20.51it/s]
+ 50%|     | 504/1000 [00:24<00:24, 20.48it/s]
+ 51%|     | 507/1000 [00:24<00:24, 20.21it/s]
+ 51%|     | 510/1000 [00:24<00:24, 20.37it/s]
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+ 52%|    | 516/1000 [00:24<00:24, 20.08it/s]
+ 52%|    | 519/1000 [00:24<00:23, 20.18it/s]
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+ 53%|    | 528/1000 [00:25<00:22, 20.56it/s]
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+ 56%|    | 563/1000 [00:27<00:21, 20.04it/s]
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+ 57%|    | 568/1000 [00:27<00:21, 19.89it/s]
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+ 57%|    | 572/1000 [00:27<00:21, 19.65it/s]
+ 57%|    | 574/1000 [00:27<00:22, 19.35it/s]
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+ 58%|    | 578/1000 [00:27<00:21, 19.32it/s]
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+ 59%|    | 589/1000 [00:28<00:20, 19.74it/s]
+ 59%|    | 592/1000 [00:28<00:20, 19.84it/s]
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+ 61%|    | 610/1000 [00:29<00:20, 19.24it/s]
+ 61%|    | 612/1000 [00:29<00:20, 19.31it/s]
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+ 62%|   | 620/1000 [00:30<00:19, 19.98it/s]
+ 62%|   | 623/1000 [00:30<00:18, 20.01it/s]
+ 63%|   | 626/1000 [00:30<00:18, 20.21it/s]
+ 63%|   | 629/1000 [00:30<00:18, 20.10it/s]
+ 63%|   | 632/1000 [00:30<00:18, 19.92it/s]
+ 63%|   | 634/1000 [00:30<00:18, 19.92it/s]
+ 64%|   | 637/1000 [00:30<00:18, 20.02it/s]
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+ 64%|   | 643/1000 [00:31<00:17, 20.12it/s]
+ 65%|   | 646/1000 [00:31<00:17, 20.52it/s]
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+ 66%|   | 655/1000 [00:31<00:16, 20.34it/s]
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+ 67%|   | 667/1000 [00:32<00:16, 19.91it/s]
+ 67%|   | 670/1000 [00:32<00:16, 20.14it/s]
+ 67%|   | 673/1000 [00:32<00:16, 20.30it/s]
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+ 69%|   | 691/1000 [00:33<00:15, 19.95it/s]
+ 69%|   | 693/1000 [00:33<00:15, 19.93it/s]
+ 70%|   | 696/1000 [00:33<00:15, 20.09it/s]
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+ 71%|   | 711/1000 [00:34<00:14, 19.90it/s]
+ 71%|  | 713/1000 [00:34<00:14, 19.87it/s]
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+ 72%|  | 722/1000 [00:35<00:14, 19.62it/s]
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+ 73%|  | 728/1000 [00:35<00:13, 19.77it/s]
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+ 74%|  | 738/1000 [00:35<00:13, 19.26it/s]
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+ 75%|  | 751/1000 [00:36<00:12, 19.45it/s]
+ 75%|  | 754/1000 [00:36<00:12, 19.58it/s]
+ 76%|  | 757/1000 [00:36<00:12, 19.78it/s]
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+ 76%|  | 765/1000 [00:37<00:11, 19.99it/s]
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+ 77%|  | 770/1000 [00:37<00:11, 19.78it/s]
+ 77%|  | 773/1000 [00:37<00:11, 19.92it/s]
+ 78%|  | 776/1000 [00:37<00:11, 19.85it/s]
+ 78%|  | 779/1000 [00:38<00:11, 20.05it/s]
+ 78%|  | 782/1000 [00:38<00:10, 20.04it/s]
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+ 79%|  | 790/1000 [00:38<00:10, 19.70it/s]
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+ 79%|  | 794/1000 [00:38<00:10, 19.56it/s]
+ 80%|  | 797/1000 [00:38<00:10, 19.72it/s]
+ 80%|  | 800/1000 [00:39<00:10, 19.75it/s]
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+ 81%|  | 812/1000 [00:39<00:09, 19.91it/s]
+ 82%| | 815/1000 [00:39<00:09, 20.00it/s]
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+ 82%| | 821/1000 [00:40<00:09, 19.68it/s]
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+ 82%| | 825/1000 [00:40<00:08, 19.61it/s]
+ 83%| | 827/1000 [00:40<00:08, 19.65it/s]
+ 83%| | 829/1000 [00:40<00:08, 19.71it/s]
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+ 83%| | 834/1000 [00:40<00:08, 19.90it/s]
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+ 86%| | 860/1000 [00:42<00:07, 19.60it/s]
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+ 87%| | 867/1000 [00:42<00:06, 19.87it/s]
+ 87%| | 869/1000 [00:42<00:06, 19.79it/s]
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+ 88%| | 877/1000 [00:42<00:06, 19.69it/s]
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+ 90%| | 898/1000 [00:44<00:05, 19.60it/s]
+ 90%| | 900/1000 [00:44<00:05, 19.69it/s]
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+ 91%| | 906/1000 [00:44<00:04, 20.07it/s]
+ 91%| | 909/1000 [00:44<00:04, 19.94it/s]
+ 91%| | 912/1000 [00:44<00:04, 20.22it/s]
+ 92%|| 915/1000 [00:44<00:04, 20.05it/s]
+ 92%|| 918/1000 [00:45<00:04, 20.06it/s]
+ 92%|| 921/1000 [00:45<00:03, 20.24it/s]
+ 92%|| 924/1000 [00:45<00:03, 20.06it/s]
+ 93%|| 927/1000 [00:45<00:03, 20.08it/s]
+ 93%|| 930/1000 [00:45<00:03, 19.95it/s]
+ 93%|| 932/1000 [00:45<00:03, 19.85it/s]
+ 93%|| 934/1000 [00:45<00:03, 19.87it/s]
+ 94%|| 937/1000 [00:45<00:03, 20.11it/s]
+ 94%|| 940/1000 [00:46<00:03, 19.85it/s]
+ 94%|| 943/1000 [00:46<00:02, 19.88it/s]
+ 94%|| 945/1000 [00:46<00:02, 19.83it/s]
+ 95%|| 947/1000 [00:46<00:02, 19.60it/s]
+ 95%|| 950/1000 [00:46<00:02, 19.82it/s]
+ 95%|| 952/1000 [00:46<00:02, 19.72it/s]
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+ 96%|| 960/1000 [00:47<00:02, 19.28it/s]
+ 96%|| 962/1000 [00:47<00:01, 19.19it/s]
+ 96%|| 964/1000 [00:47<00:01, 19.29it/s]
+ 97%|| 966/1000 [00:47<00:01, 19.39it/s]
+ 97%|| 969/1000 [00:47<00:01, 19.80it/s]
+ 97%|| 972/1000 [00:47<00:01, 19.94it/s]
+ 97%|| 974/1000 [00:47<00:01, 19.71it/s]
+ 98%|| 976/1000 [00:47<00:01, 19.64it/s]
+ 98%|| 978/1000 [00:48<00:01, 19.60it/s]
+ 98%|| 981/1000 [00:48<00:00, 19.71it/s]
+ 98%|| 984/1000 [00:48<00:00, 19.68it/s]
+ 99%|| 986/1000 [00:48<00:00, 19.71it/s]
+ 99%|| 988/1000 [00:48<00:00, 19.76it/s]
+ 99%|| 991/1000 [00:48<00:00, 19.79it/s]
+ 99%|| 993/1000 [00:48<00:00, 19.80it/s]
+100%|| 996/1000 [00:48<00:00, 19.94it/s]
+100%|| 998/1000 [00:49<00:00, 19.86it/s]
+100%|| 1000/1000 [00:49<00:00, 20.33it/s]
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/scikit-learn/1.3.1-gfbf-2023a/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target
+  warnings.warn(msg, UserWarning)
+FAILED
+tests/plugins/privacy/test_decaf.py::test_get_dag[hillclimb] PASSED
+tests/plugins/privacy/test_decaf.py::test_get_dag[d-struct] 
+Training: |          | 0/? [00:00<?, ?it/s]
+Training:   0%|          | 0/1 [00:00<?, ?it/s]
+Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s] 
+Epoch 0: 100%|| 1/1 [00:03<00:00,  0.28it/s]
+Epoch 0: 100%|| 1/1 [00:03<00:00,  0.28it/s, v_num=2.01e+7, mse_loss=0.821, dsl_loss=0.377, total_loss=1.200]
+Epoch 0: 100%|| 1/1 [00:03<00:00,  0.28it/s, v_num=2.01e+7, mse_loss=0.821, dsl_loss=0.377, total_loss=1.200]
+Epoch 0: 100%|| 1/1 [00:03<00:00,  0.28it/s, v_num=2.01e+7, mse_loss=0.821, dsl_loss=0.377, total_loss=1.200]
+PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_fit[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  6.14it/s]
+ 20%|        | 2/10 [00:00<00:01,  6.90it/s]
+ 30%|       | 3/10 [00:00<00:00,  7.38it/s]
+ 40%|      | 4/10 [00:00<00:00,  7.39it/s]
+ 50%|     | 5/10 [00:00<00:00,  7.62it/s]
+ 60%|    | 6/10 [00:00<00:00,  7.58it/s]
+ 70%|   | 7/10 [00:00<00:00,  7.68it/s]
+ 80%|  | 8/10 [00:01<00:00,  7.67it/s]
+ 90%| | 9/10 [00:01<00:00,  7.69it/s]
+100%|| 10/10 [00:01<00:00,  7.64it/s]
+100%|| 10/10 [00:01<00:00,  7.51it/s]
+PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_fit[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  6.07it/s]
+ 20%|        | 2/10 [00:00<00:01,  6.86it/s]
+ 30%|       | 3/10 [00:00<00:00,  7.08it/s]
+ 40%|      | 4/10 [00:00<00:00,  7.29it/s]
+ 50%|     | 5/10 [00:00<00:00,  7.32it/s]
+ 60%|    | 6/10 [00:00<00:00,  7.39it/s]
+ 70%|   | 7/10 [00:00<00:00,  7.54it/s]
+ 80%|  | 8/10 [00:01<00:00,  7.61it/s]
+ 90%| | 9/10 [00:01<00:00,  7.53it/s]
+100%|| 10/10 [00:01<00:00,  7.57it/s]
+100%|| 10/10 [00:01<00:00,  7.38it/s]
+PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_fit[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  5.20it/s]
+ 20%|        | 2/10 [00:00<00:01,  6.02it/s]
+ 30%|       | 3/10 [00:00<00:01,  6.40it/s]
+ 40%|      | 4/10 [00:00<00:00,  6.55it/s]
+ 50%|     | 5/10 [00:00<00:00,  6.70it/s]
+ 60%|    | 6/10 [00:00<00:00,  6.85it/s]
+ 70%|   | 7/10 [00:01<00:00,  6.82it/s]
+ 80%|  | 8/10 [00:01<00:00,  6.83it/s]
+ 90%| | 9/10 [00:01<00:00,  6.94it/s]
+100%|| 10/10 [00:01<00:00,  6.89it/s]
+100%|| 10/10 [00:01<00:00,  6.69it/s]
+PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:01,  6.11it/s]
+ 20%|        | 2/10 [00:00<00:01,  6.38it/s]
+ 30%|       | 3/10 [00:00<00:01,  6.69it/s]
+ 40%|      | 4/10 [00:00<00:00,  6.68it/s]
+ 50%|     | 5/10 [00:00<00:00,  6.76it/s]
+ 60%|    | 6/10 [00:00<00:00,  6.81it/s]
+ 70%|   | 7/10 [00:01<00:00,  6.85it/s]
+ 80%|  | 8/10 [00:01<00:00,  6.85it/s]
+ 90%| | 9/10 [00:01<00:00,  6.91it/s]
+100%|| 10/10 [00:01<00:00,  6.92it/s]
+100%|| 10/10 [00:01<00:00,  6.79it/s]
+PASSED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+  0%|          | 0/10 [00:00<?, ?it/s]
+FAILED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+  0%|          | 0/10 [00:00<?, ?it/s]
+FAILED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+  0%|          | 0/10 [00:00<?, ?it/s]
+FAILED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+  0%|          | 0/10 [00:00<?, ?it/s]
+FAILED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+  0%|          | 0/10 [00:00<?, ?it/s]
+FAILED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+  0%|          | 0/10 [00:00<?, ?it/s]
+FAILED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+  0%|          | 0/10 [00:00<?, ?it/s]
+FAILED
+tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+  0%|          | 0/10 [00:00<?, ?it/s]
+FAILED
+tests/plugins/privacy/test_dpgan.py::test_sample_hyperparams PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_fit[test_plugin0] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 20%|        | 2/10 [00:00<00:00, 14.22it/s]
+ 40%|      | 4/10 [00:00<00:00, 13.93it/s]
+ 60%|    | 6/10 [00:00<00:00, 14.18it/s]
+ 80%|  | 8/10 [00:00<00:00, 13.45it/s]
+100%|| 10/10 [00:00<00:00, 13.65it/s]
+100%|| 10/10 [00:00<00:00, 13.74it/s]
+PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_fit[test_plugin1] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 20%|        | 2/10 [00:00<00:00, 13.06it/s]
+ 40%|      | 4/10 [00:00<00:00, 14.50it/s]
+ 60%|    | 6/10 [00:00<00:00, 14.41it/s]
+ 80%|  | 8/10 [00:00<00:00, 14.63it/s]
+100%|| 10/10 [00:00<00:00, 15.09it/s]
+100%|| 10/10 [00:00<00:00, 14.73it/s]
+PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_fit[test_plugin2] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:00,  9.45it/s]
+ 30%|       | 3/10 [00:00<00:00, 13.03it/s]
+ 50%|     | 5/10 [00:00<00:00, 13.72it/s]
+ 70%|   | 7/10 [00:00<00:00, 14.76it/s]
+ 90%| | 9/10 [00:00<00:00, 15.21it/s]
+100%|| 10/10 [00:00<00:00, 14.47it/s]
+PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_generate_pategan 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:00<00:04,  1.82it/s]
+ 20%|        | 2/10 [00:01<00:04,  1.85it/s]
+ 30%|       | 3/10 [00:01<00:03,  1.86it/s]
+ 40%|      | 4/10 [00:02<00:03,  1.88it/s]
+ 50%|     | 5/10 [00:02<00:02,  1.93it/s]
+ 60%|    | 6/10 [00:03<00:02,  1.93it/s]
+ 70%|   | 7/10 [00:03<00:01,  1.87it/s]
+ 80%|  | 8/10 [00:04<00:01,  1.89it/s]
+ 90%| | 9/10 [00:04<00:00,  1.86it/s]
+100%|| 10/10 [00:05<00:00,  1.88it/s]
+100%|| 10/10 [00:05<00:00,  1.88it/s]
+PASSED
+tests/plugins/privacy/test_pategan.py::test_plugin_generate_constraints 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 20%|        | 2/10 [00:00<00:00, 13.85it/s]
+ 40%|      | 4/10 [00:00<00:00, 14.79it/s]
+ 60%|    | 6/10 [00:00<00:00, 15.47it/s]
+ 80%|  | 8/10 [00:00<00:00, 15.94it/s]
+100%|| 10/10 [00:00<00:00, 15.32it/s]
+100%|| 10/10 [00:00<00:00, 15.27it/s]
+PASSED
+tests/plugins/privacy/test_pategan.py::test_sample_hyperparams PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_type[test_plugin1] PASSED
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+tests/plugins/privacy/test_privbayes.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/privacy/test_privbayes.py::test_plugin_hyperparams[test_plugin2] PASSED
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+  0%|          | 0/3 [00:00<?, ?it/s]
+ 33%|      | 1/3 [00:00<00:00,  8.50it/s]
+100%|| 3/3 [00:00<00:00, 13.92it/s]
+100%|| 3/3 [00:00<00:00, 13.07it/s]
+PASSED
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+  0%|          | 0/3 [00:00<?, ?it/s]
+ 33%|      | 1/3 [00:00<00:00,  9.53it/s]
+100%|| 3/3 [00:00<00:00, 14.56it/s]
+100%|| 3/3 [00:00<00:00, 13.82it/s]
+PASSED
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+  0%|          | 0/3 [00:00<?, ?it/s]
+ 33%|      | 1/3 [00:00<00:00,  9.57it/s]
+100%|| 3/3 [00:00<00:00, 14.60it/s]
+100%|| 3/3 [00:00<00:00, 13.86it/s]
+PASSED
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+  0%|          | 0/3 [00:00<?, ?it/s]
+ 33%|      | 1/3 [00:00<00:00,  9.92it/s]
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+100%|| 3/3 [00:00<00:00, 12.51it/s]
+
+  0%|          | 0/4 [00:00<?, ?it/s]
+Generating for node: sepal length (cm):   0%|          | 0/4 [00:00<?, ?it/s]
+Generating for node: sepal width (cm):   0%|          | 0/4 [00:00<?, ?it/s] 
+Generating for node: petal width (cm):   0%|          | 0/4 [00:00<?, ?it/s]
+Generating for node: petal length (cm):   0%|          | 0/4 [00:00<?, ?it/s]
+Generating for node: petal length (cm): 100%|| 4/4 [00:00<00:00, 300.46it/s]
+PASSED
+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_sanity[test_plugin0] PASSED
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+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_name[test_plugin1] PASSED
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+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_type[test_plugin2] PASSED
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+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_fit[test_plugin1] PASSED
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+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_generate[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_generate[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_generic_marginal_distributions.py::test_plugin_generate[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_sanity[test_plugin2] PASSED
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+tests/plugins/survival_analysis/test_survae.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_type[test_plugin2] PASSED
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+tests/plugins/survival_analysis/test_survae.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_generate[uncensoring] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:17,  5.72it/s]
+  2%|         | 2/100 [00:00<00:14,  6.79it/s]
+  3%|         | 3/100 [00:00<00:13,  7.26it/s]
+  4%|         | 4/100 [00:00<00:12,  7.52it/s]
+  5%|         | 5/100 [00:00<00:12,  7.54it/s]
+  6%|         | 6/100 [00:00<00:12,  7.74it/s]
+  7%|         | 7/100 [00:00<00:11,  7.88it/s]
+  8%|         | 8/100 [00:01<00:11,  8.09it/s]
+  9%|         | 9/100 [00:01<00:11,  8.11it/s]
+ 10%|         | 10/100 [00:01<00:11,  8.01it/s]
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+ 13%|        | 13/100 [00:01<00:10,  8.15it/s]
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+ 31%|       | 31/100 [00:03<00:08,  8.16it/s]
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+100%|| 100/100 [00:12<00:00,  7.91it/s]
+100%|| 100/100 [00:12<00:00,  8.05it/s]
+PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_generate[survival_function] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:16,  5.86it/s]
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+100%|| 100/100 [00:12<00:00,  7.47it/s]
+100%|| 100/100 [00:12<00:00,  7.75it/s]
+PASSED
+tests/plugins/survival_analysis/test_survae.py::test_sample_hyperparams PASSED
+tests/plugins/survival_analysis/test_survae.py::test_plugin_generate_with_conditional_survae 
+  0%|          | 0/1000 [00:00<?, ?it/s]
+  0%|          | 1/1000 [00:00<02:35,  6.44it/s]
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+  1%|          | 10/1000 [00:01<01:54,  8.64it/s]
+  1%|          | 11/1000 [00:01<01:53,  8.70it/s]
+  1%|          | 12/1000 [00:01<01:54,  8.65it/s]
+  1%|         | 13/1000 [00:01<01:54,  8.60it/s]
+  1%|         | 14/1000 [00:01<01:54,  8.62it/s]
+  2%|         | 15/1000 [00:01<01:56,  8.46it/s]
+  2%|         | 16/1000 [00:01<01:55,  8.50it/s]
+  2%|         | 17/1000 [00:02<01:57,  8.39it/s]
+  2%|         | 18/1000 [00:02<01:57,  8.34it/s]
+  2%|         | 19/1000 [00:02<01:59,  8.20it/s]
+  2%|         | 20/1000 [00:02<01:58,  8.29it/s]
+  2%|         | 21/1000 [00:02<01:59,  8.22it/s]
+  2%|         | 22/1000 [00:02<01:58,  8.28it/s]
+  2%|         | 23/1000 [00:02<01:55,  8.44it/s]
+  2%|         | 24/1000 [00:02<01:57,  8.32it/s]
+  2%|         | 25/1000 [00:02<01:57,  8.27it/s]
+  3%|         | 26/1000 [00:03<01:55,  8.42it/s]
+  3%|         | 27/1000 [00:03<01:55,  8.40it/s]
+  3%|         | 28/1000 [00:03<01:54,  8.51it/s]
+  3%|         | 29/1000 [00:03<01:54,  8.47it/s]
+  3%|         | 30/1000 [00:03<01:53,  8.55it/s]
+  3%|         | 31/1000 [00:03<01:53,  8.56it/s]
+  3%|         | 32/1000 [00:03<01:51,  8.65it/s]
+  3%|         | 33/1000 [00:03<01:51,  8.66it/s]
+  3%|         | 34/1000 [00:04<01:51,  8.67it/s]
+  4%|         | 35/1000 [00:04<01:52,  8.58it/s]
+  4%|         | 36/1000 [00:04<01:51,  8.65it/s]
+  4%|         | 37/1000 [00:04<01:52,  8.56it/s]
+  4%|         | 38/1000 [00:04<01:51,  8.59it/s]
+  4%|         | 39/1000 [00:04<01:52,  8.53it/s]
+  4%|         | 40/1000 [00:04<01:52,  8.57it/s]
+  4%|         | 41/1000 [00:04<01:51,  8.61it/s]
+  4%|         | 42/1000 [00:04<01:50,  8.66it/s]
+  4%|         | 43/1000 [00:05<01:51,  8.60it/s]
+  4%|         | 44/1000 [00:05<01:51,  8.61it/s]
+  4%|         | 45/1000 [00:05<01:51,  8.54it/s]
+  5%|         | 46/1000 [00:05<01:52,  8.49it/s]
+  5%|         | 47/1000 [00:05<01:51,  8.58it/s]
+  5%|         | 48/1000 [00:05<01:51,  8.56it/s]
+  5%|         | 49/1000 [00:05<01:50,  8.61it/s]
+  5%|         | 50/1000 [00:05<01:50,  8.60it/s]
+  5%|         | 51/1000 [00:06<01:55,  8.22it/s]
+  5%|         | 52/1000 [00:06<01:53,  8.36it/s]
+  5%|         | 53/1000 [00:06<01:52,  8.39it/s]
+  5%|         | 54/1000 [00:06<01:51,  8.45it/s]
+  6%|         | 55/1000 [00:06<01:51,  8.48it/s]
+  6%|         | 56/1000 [00:06<01:51,  8.46it/s]
+  6%|         | 57/1000 [00:06<01:51,  8.48it/s]
+  6%|         | 58/1000 [00:06<01:51,  8.45it/s]
+  6%|         | 59/1000 [00:06<01:49,  8.58it/s]
+  6%|         | 60/1000 [00:07<01:49,  8.58it/s]
+  6%|         | 61/1000 [00:07<01:50,  8.50it/s]
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+  6%|         | 63/1000 [00:07<01:50,  8.48it/s]
+  6%|         | 64/1000 [00:07<01:50,  8.44it/s]
+  6%|         | 65/1000 [00:07<01:53,  8.23it/s]
+  7%|         | 66/1000 [00:07<01:52,  8.29it/s]
+  7%|         | 67/1000 [00:07<01:52,  8.26it/s]
+  7%|         | 68/1000 [00:08<01:52,  8.27it/s]
+  7%|         | 69/1000 [00:08<01:55,  8.08it/s]
+  7%|         | 70/1000 [00:08<01:56,  7.96it/s]
+  7%|         | 71/1000 [00:08<01:55,  8.06it/s]
+  7%|         | 72/1000 [00:08<01:53,  8.20it/s]
+  7%|         | 73/1000 [00:08<01:51,  8.30it/s]
+  7%|         | 74/1000 [00:08<01:53,  8.18it/s]
+  8%|         | 75/1000 [00:08<01:53,  8.18it/s]
+  8%|         | 76/1000 [00:09<01:53,  8.14it/s]
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+  8%|         | 78/1000 [00:09<01:54,  8.08it/s]
+  8%|         | 79/1000 [00:09<01:57,  7.84it/s]
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+  8%|         | 81/1000 [00:09<01:54,  8.01it/s]
+  8%|         | 82/1000 [00:09<01:54,  8.01it/s]
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+  8%|         | 84/1000 [00:10<01:53,  8.08it/s]
+  8%|         | 85/1000 [00:10<01:53,  8.09it/s]
+  9%|         | 86/1000 [00:10<01:52,  8.14it/s]
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+  9%|         | 88/1000 [00:10<01:54,  7.98it/s]
+  9%|         | 89/1000 [00:10<01:53,  8.01it/s]
+  9%|         | 90/1000 [00:10<01:54,  7.96it/s]
+  9%|         | 91/1000 [00:10<01:56,  7.81it/s]
+  9%|         | 92/1000 [00:11<01:55,  7.88it/s]
+  9%|         | 93/1000 [00:11<01:54,  7.90it/s]
+  9%|         | 94/1000 [00:11<01:54,  7.93it/s]
+ 10%|         | 95/1000 [00:11<01:56,  7.75it/s]
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+ 45%|     | 450/1000 [00:58<01:11,  7.65it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_fit[survival_function] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.27it/s]
+  3%|         | 3/100 [00:00<00:08, 11.23it/s]
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+ 99%|| 99/100 [00:09<00:00, 11.52it/s]
+100%|| 100/100 [00:10<00:00,  9.36it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_fit[uncensoring] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:11,  8.56it/s]
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+ 99%|| 99/100 [00:09<00:00, 12.01it/s]
+100%|| 100/100 [00:10<00:00,  9.40it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_generate[uncensoring] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  2%|         | 2/100 [00:00<00:08, 11.22it/s]
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+ 32%|      | 32/100 [00:02<00:05, 12.22it/s]
+ 34%|      | 34/100 [00:02<00:05, 12.39it/s]
+ 36%|      | 36/100 [00:02<00:05, 12.64it/s]
+ 38%|      | 38/100 [00:03<00:04, 12.56it/s]
+ 40%|      | 40/100 [00:03<00:04, 12.44it/s]
+ 42%|     | 42/100 [00:03<00:04, 12.51it/s]
+ 44%|     | 44/100 [00:03<00:04, 12.47it/s]
+ 46%|     | 46/100 [00:03<00:04, 12.35it/s]
+ 48%|     | 48/100 [00:03<00:04, 12.47it/s]
+ 50%|     | 50/100 [00:04<00:04, 11.76it/s]
+ 52%|    | 52/100 [00:04<00:04, 11.52it/s]
+ 54%|    | 54/100 [00:04<00:03, 11.72it/s]
+ 56%|    | 56/100 [00:04<00:03, 11.88it/s]
+ 58%|    | 58/100 [00:04<00:03, 12.20it/s]
+ 60%|    | 60/100 [00:04<00:03, 12.05it/s]
+ 62%|   | 62/100 [00:05<00:03, 12.26it/s]
+ 64%|   | 64/100 [00:05<00:03, 11.88it/s]
+ 66%|   | 66/100 [00:05<00:02, 12.02it/s]
+ 68%|   | 68/100 [00:05<00:02, 11.85it/s]
+ 70%|   | 70/100 [00:05<00:02, 12.11it/s]
+ 72%|  | 72/100 [00:05<00:02, 12.16it/s]
+ 74%|  | 74/100 [00:06<00:02, 12.34it/s]
+ 76%|  | 76/100 [00:06<00:01, 12.27it/s]
+ 78%|  | 78/100 [00:06<00:01, 12.11it/s]
+ 80%|  | 80/100 [00:06<00:01, 12.31it/s]
+ 82%| | 82/100 [00:06<00:01, 12.42it/s]
+ 84%| | 84/100 [00:06<00:01, 12.41it/s]
+ 86%| | 86/100 [00:06<00:01, 12.37it/s]
+ 88%| | 88/100 [00:07<00:00, 12.39it/s]
+ 90%| | 90/100 [00:07<00:00, 11.84it/s]
+ 92%|| 92/100 [00:07<00:00, 12.07it/s]
+ 94%|| 94/100 [00:07<00:00, 11.97it/s]
+ 96%|| 96/100 [00:07<00:00, 11.94it/s]
+ 98%|| 98/100 [00:08<00:00, 11.97it/s]
+100%|| 100/100 [00:09<00:00,  4.11it/s]
+100%|| 100/100 [00:09<00:00, 10.82it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_generate[survival_function] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:11,  8.44it/s]
+  3%|         | 3/100 [00:00<00:09, 10.32it/s]
+  5%|         | 5/100 [00:00<00:08, 11.21it/s]
+  7%|         | 7/100 [00:00<00:08, 11.60it/s]
+  9%|         | 9/100 [00:00<00:07, 11.95it/s]
+ 11%|         | 11/100 [00:00<00:07, 11.99it/s]
+ 13%|        | 13/100 [00:01<00:07, 12.12it/s]
+ 15%|        | 15/100 [00:01<00:06, 12.17it/s]
+ 17%|        | 17/100 [00:01<00:06, 12.25it/s]
+ 19%|        | 19/100 [00:01<00:06, 11.74it/s]
+ 21%|        | 21/100 [00:01<00:06, 11.72it/s]
+ 23%|       | 23/100 [00:01<00:06, 11.69it/s]
+ 25%|       | 25/100 [00:02<00:06, 11.82it/s]
+ 27%|       | 27/100 [00:02<00:06, 11.75it/s]
+ 29%|       | 29/100 [00:02<00:05, 12.06it/s]
+ 31%|       | 31/100 [00:02<00:05, 11.99it/s]
+ 33%|      | 33/100 [00:02<00:05, 11.87it/s]
+ 35%|      | 35/100 [00:02<00:05, 11.95it/s]
+ 37%|      | 37/100 [00:03<00:05, 12.08it/s]
+ 39%|      | 39/100 [00:03<00:05, 12.10it/s]
+ 41%|      | 41/100 [00:03<00:04, 12.26it/s]
+ 43%|     | 43/100 [00:03<00:04, 12.24it/s]
+ 45%|     | 45/100 [00:03<00:04, 12.23it/s]
+ 47%|     | 47/100 [00:03<00:04, 12.22it/s]
+ 49%|     | 49/100 [00:04<00:04, 12.11it/s]
+ 51%|     | 51/100 [00:04<00:04, 11.38it/s]
+ 53%|    | 53/100 [00:04<00:03, 11.76it/s]
+ 55%|    | 55/100 [00:04<00:03, 11.75it/s]
+ 57%|    | 57/100 [00:04<00:03, 11.80it/s]
+ 59%|    | 59/100 [00:04<00:03, 11.90it/s]
+ 61%|    | 61/100 [00:05<00:03, 11.92it/s]
+ 63%|   | 63/100 [00:05<00:03, 11.93it/s]
+ 65%|   | 65/100 [00:05<00:03, 11.60it/s]
+ 67%|   | 67/100 [00:05<00:02, 11.70it/s]
+ 69%|   | 69/100 [00:05<00:02, 11.91it/s]
+ 71%|   | 71/100 [00:05<00:02, 11.85it/s]
+ 73%|  | 73/100 [00:06<00:02, 12.06it/s]
+ 75%|  | 75/100 [00:06<00:02, 11.60it/s]
+ 77%|  | 77/100 [00:06<00:01, 11.53it/s]
+ 79%|  | 79/100 [00:06<00:01, 11.59it/s]
+ 81%|  | 81/100 [00:06<00:01, 11.67it/s]
+ 83%| | 83/100 [00:07<00:01, 11.72it/s]
+ 85%| | 85/100 [00:07<00:01, 11.97it/s]
+ 87%| | 87/100 [00:07<00:01, 11.78it/s]
+ 89%| | 89/100 [00:07<00:00, 11.80it/s]
+ 91%| | 91/100 [00:07<00:00, 11.78it/s]
+ 93%|| 93/100 [00:07<00:00, 11.90it/s]
+ 95%|| 95/100 [00:08<00:00, 11.93it/s]
+ 97%|| 97/100 [00:08<00:00, 11.79it/s]
+ 99%|| 99/100 [00:08<00:00, 11.78it/s]
+100%|| 100/100 [00:09<00:00, 10.51it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_survival_plugin_generate_constraints[uncensoring] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:11,  8.89it/s]
+  3%|         | 3/100 [00:00<00:08, 11.15it/s]
+  5%|         | 5/100 [00:00<00:08, 11.70it/s]
+  7%|         | 7/100 [00:00<00:07, 12.14it/s]
+  9%|         | 9/100 [00:00<00:07, 11.88it/s]
+ 11%|         | 11/100 [00:00<00:07, 12.11it/s]
+ 13%|        | 13/100 [00:01<00:07, 12.16it/s]
+ 15%|        | 15/100 [00:01<00:07, 12.13it/s]
+ 17%|        | 17/100 [00:01<00:07, 11.78it/s]
+ 19%|        | 19/100 [00:01<00:06, 11.71it/s]
+ 21%|        | 21/100 [00:01<00:06, 11.82it/s]
+ 23%|       | 23/100 [00:01<00:06, 12.09it/s]
+ 25%|       | 25/100 [00:02<00:06, 11.95it/s]
+ 27%|       | 27/100 [00:02<00:06, 12.08it/s]
+ 29%|       | 29/100 [00:02<00:06, 11.82it/s]
+ 31%|       | 31/100 [00:02<00:05, 11.94it/s]
+ 33%|      | 33/100 [00:02<00:05, 11.99it/s]
+ 35%|      | 35/100 [00:02<00:05, 12.04it/s]
+ 37%|      | 37/100 [00:03<00:05, 12.05it/s]
+ 39%|      | 39/100 [00:03<00:05, 11.94it/s]
+ 41%|      | 41/100 [00:03<00:04, 12.23it/s]
+ 43%|     | 43/100 [00:03<00:04, 12.18it/s]
+ 45%|     | 45/100 [00:03<00:04, 12.38it/s]
+ 47%|     | 47/100 [00:03<00:04, 12.27it/s]
+ 49%|     | 49/100 [00:04<00:04, 12.27it/s]
+ 51%|     | 51/100 [00:04<00:04, 11.80it/s]
+ 53%|    | 53/100 [00:04<00:04, 11.61it/s]
+ 55%|    | 55/100 [00:04<00:03, 11.88it/s]
+ 57%|    | 57/100 [00:04<00:03, 12.09it/s]
+ 59%|    | 59/100 [00:04<00:03, 12.28it/s]
+ 61%|    | 61/100 [00:05<00:03, 12.22it/s]
+ 63%|   | 63/100 [00:05<00:03, 12.06it/s]
+ 65%|   | 65/100 [00:05<00:02, 12.42it/s]
+ 67%|   | 67/100 [00:05<00:02, 12.52it/s]
+ 69%|   | 69/100 [00:05<00:02, 12.40it/s]
+ 71%|   | 71/100 [00:05<00:02, 12.60it/s]
+ 73%|  | 73/100 [00:06<00:02, 12.35it/s]
+ 75%|  | 75/100 [00:06<00:02, 12.14it/s]
+ 77%|  | 77/100 [00:06<00:01, 12.16it/s]
+ 79%|  | 79/100 [00:06<00:01, 11.87it/s]
+ 81%|  | 81/100 [00:06<00:01, 12.19it/s]
+ 83%| | 83/100 [00:06<00:01, 12.27it/s]
+ 85%| | 85/100 [00:07<00:01, 12.30it/s]
+ 87%| | 87/100 [00:07<00:01, 12.25it/s]
+ 89%| | 89/100 [00:07<00:00, 12.25it/s]
+ 91%| | 91/100 [00:07<00:00, 12.27it/s]
+ 93%|| 93/100 [00:07<00:00, 12.08it/s]
+ 95%|| 95/100 [00:07<00:00, 12.10it/s]
+ 97%|| 97/100 [00:08<00:00, 12.30it/s]
+ 99%|| 99/100 [00:08<00:00, 12.07it/s]
+100%|| 100/100 [00:08<00:00, 12.06it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_survival_plugin_generate_constraints[survival_function] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:12,  8.12it/s]
+  3%|         | 3/100 [00:00<00:09, 10.61it/s]
+  5%|         | 5/100 [00:00<00:08, 11.18it/s]
+  7%|         | 7/100 [00:00<00:08, 11.59it/s]
+  9%|         | 9/100 [00:00<00:07, 11.59it/s]
+ 11%|         | 11/100 [00:00<00:07, 11.57it/s]
+ 13%|        | 13/100 [00:01<00:07, 11.71it/s]
+ 15%|        | 15/100 [00:01<00:07, 11.83it/s]
+ 17%|        | 17/100 [00:01<00:06, 11.96it/s]
+ 19%|        | 19/100 [00:01<00:06, 12.16it/s]
+ 21%|        | 21/100 [00:01<00:06, 12.43it/s]
+ 23%|       | 23/100 [00:01<00:06, 12.45it/s]
+ 25%|       | 25/100 [00:02<00:06, 12.41it/s]
+ 27%|       | 27/100 [00:02<00:06, 12.06it/s]
+ 29%|       | 29/100 [00:02<00:05, 12.16it/s]
+ 31%|       | 31/100 [00:02<00:05, 12.07it/s]
+ 33%|      | 33/100 [00:02<00:05, 11.99it/s]
+ 35%|      | 35/100 [00:02<00:05, 12.01it/s]
+ 37%|      | 37/100 [00:03<00:05, 12.12it/s]
+ 39%|      | 39/100 [00:03<00:05, 12.03it/s]
+ 41%|      | 41/100 [00:03<00:05, 11.78it/s]
+ 43%|     | 43/100 [00:03<00:04, 11.74it/s]
+ 45%|     | 45/100 [00:03<00:04, 11.87it/s]
+ 47%|     | 47/100 [00:03<00:04, 11.89it/s]
+ 49%|     | 49/100 [00:04<00:04, 12.12it/s]
+ 51%|     | 51/100 [00:04<00:04, 11.38it/s]
+ 53%|    | 53/100 [00:04<00:04, 11.61it/s]
+ 55%|    | 55/100 [00:04<00:03, 11.42it/s]
+ 57%|    | 57/100 [00:04<00:03, 11.62it/s]
+ 59%|    | 59/100 [00:04<00:03, 11.92it/s]
+ 61%|    | 61/100 [00:05<00:03, 11.95it/s]
+ 63%|   | 63/100 [00:05<00:03, 12.09it/s]
+ 65%|   | 65/100 [00:05<00:02, 12.14it/s]
+ 67%|   | 67/100 [00:05<00:02, 12.11it/s]
+ 69%|   | 69/100 [00:05<00:02, 12.07it/s]
+ 71%|   | 71/100 [00:05<00:02, 12.16it/s]
+ 73%|  | 73/100 [00:06<00:02, 11.92it/s]
+ 75%|  | 75/100 [00:06<00:02, 11.90it/s]
+ 77%|  | 77/100 [00:06<00:01, 11.87it/s]
+ 79%|  | 79/100 [00:06<00:01, 11.58it/s]
+ 81%|  | 81/100 [00:06<00:01, 11.66it/s]
+ 83%| | 83/100 [00:07<00:01, 11.49it/s]
+ 85%| | 85/100 [00:07<00:01, 11.75it/s]
+ 87%| | 87/100 [00:07<00:01, 11.60it/s]
+ 89%| | 89/100 [00:07<00:00, 11.76it/s]
+ 91%| | 91/100 [00:07<00:00, 12.04it/s]
+ 93%|| 93/100 [00:07<00:00, 11.98it/s]
+ 95%|| 95/100 [00:08<00:00, 11.95it/s]
+ 97%|| 97/100 [00:08<00:00, 11.89it/s]
+ 99%|| 99/100 [00:08<00:00, 11.90it/s]
+100%|| 100/100 [00:08<00:00, 11.84it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_sample_hyperparams PASSED
+tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_generate_with_conditional 
+  0%|          | 0/2000 [00:00<?, ?it/s]
+  0%|          | 1/2000 [00:00<04:41,  7.09it/s]
+  0%|          | 2/2000 [00:00<04:13,  7.87it/s]
+  0%|          | 3/2000 [00:00<04:03,  8.20it/s]
+  0%|          | 4/2000 [00:00<03:58,  8.38it/s]
+  0%|          | 5/2000 [00:00<03:53,  8.53it/s]
+  0%|          | 6/2000 [00:00<03:53,  8.54it/s]
+  0%|          | 7/2000 [00:00<03:57,  8.39it/s]
+  0%|          | 8/2000 [00:00<03:58,  8.35it/s]
+  0%|          | 9/2000 [00:01<03:56,  8.43it/s]
+  0%|          | 10/2000 [00:01<03:54,  8.48it/s]
+  1%|          | 11/2000 [00:01<03:54,  8.48it/s]
+  1%|          | 12/2000 [00:01<03:53,  8.50it/s]
+  1%|          | 13/2000 [00:01<03:53,  8.52it/s]
+  1%|          | 14/2000 [00:01<03:51,  8.59it/s]
+  1%|          | 15/2000 [00:01<03:50,  8.60it/s]
+  1%|          | 16/2000 [00:01<03:52,  8.54it/s]
+  1%|          | 17/2000 [00:02<03:52,  8.54it/s]
+  1%|          | 18/2000 [00:02<03:52,  8.54it/s]
+  1%|          | 19/2000 [00:02<03:50,  8.59it/s]
+  1%|          | 20/2000 [00:02<03:47,  8.71it/s]
+  1%|          | 21/2000 [00:02<03:48,  8.64it/s]
+  1%|          | 22/2000 [00:02<03:49,  8.61it/s]
+  1%|          | 23/2000 [00:02<03:49,  8.63it/s]
+  1%|          | 24/2000 [00:02<03:49,  8.62it/s]
+  1%|         | 25/2000 [00:02<03:52,  8.51it/s]
+  1%|         | 26/2000 [00:03<03:52,  8.49it/s]
+  1%|         | 27/2000 [00:03<03:50,  8.55it/s]
+  1%|         | 28/2000 [00:03<03:51,  8.51it/s]
+  1%|         | 29/2000 [00:03<03:48,  8.63it/s]
+  2%|         | 30/2000 [00:03<03:44,  8.76it/s]
+  2%|         | 31/2000 [00:03<03:43,  8.79it/s]
+  2%|         | 32/2000 [00:03<03:43,  8.80it/s]
+  2%|         | 33/2000 [00:03<03:43,  8.81it/s]
+  2%|         | 34/2000 [00:03<03:42,  8.85it/s]
+  2%|         | 35/2000 [00:04<03:42,  8.83it/s]
+  2%|         | 36/2000 [00:04<03:45,  8.72it/s]
+  2%|         | 37/2000 [00:04<03:44,  8.75it/s]
+  2%|         | 38/2000 [00:04<03:47,  8.63it/s]
+  2%|         | 39/2000 [00:04<03:48,  8.60it/s]
+  2%|         | 40/2000 [00:04<03:50,  8.52it/s]
+  2%|         | 41/2000 [00:04<03:49,  8.55it/s]
+  2%|         | 42/2000 [00:04<03:46,  8.66it/s]
+  2%|         | 43/2000 [00:05<03:51,  8.46it/s]
+  2%|         | 44/2000 [00:05<03:51,  8.46it/s]
+  2%|         | 45/2000 [00:05<03:49,  8.51it/s]
+  2%|         | 46/2000 [00:05<03:48,  8.56it/s]
+  2%|         | 47/2000 [00:05<03:44,  8.69it/s]
+  2%|         | 48/2000 [00:05<03:44,  8.68it/s]
+  2%|         | 49/2000 [00:05<03:46,  8.61it/s]
+  2%|         | 50/2000 [00:06<14:25,  2.25it/s]
+  3%|         | 51/2000 [00:07<11:30,  2.82it/s]
+  3%|         | 52/2000 [00:07<09:12,  3.52it/s]
+  3%|         | 53/2000 [00:07<07:31,  4.31it/s]
+  3%|         | 54/2000 [00:07<06:26,  5.04it/s]
+  3%|         | 55/2000 [00:07<05:40,  5.71it/s]
+  3%|         | 56/2000 [00:07<05:06,  6.34it/s]
+  3%|         | 57/2000 [00:07<04:40,  6.92it/s]
+  3%|         | 58/2000 [00:07<04:20,  7.46it/s]
+  3%|         | 59/2000 [00:08<04:08,  7.80it/s]
+  3%|         | 60/2000 [00:08<04:00,  8.05it/s]
+  3%|         | 61/2000 [00:08<03:59,  8.11it/s]
+  3%|         | 62/2000 [00:08<03:53,  8.29it/s]
+  3%|         | 63/2000 [00:08<03:53,  8.31it/s]
+  3%|         | 64/2000 [00:08<03:51,  8.35it/s]
+  3%|         | 65/2000 [00:08<03:46,  8.55it/s]
+  3%|         | 66/2000 [00:08<03:42,  8.69it/s]
+  3%|         | 67/2000 [00:08<03:44,  8.63it/s]
+  3%|         | 68/2000 [00:09<03:41,  8.71it/s]
+  3%|         | 69/2000 [00:09<03:42,  8.66it/s]
+  4%|         | 70/2000 [00:09<03:43,  8.65it/s]
+  4%|         | 71/2000 [00:09<03:43,  8.63it/s]
+  4%|         | 72/2000 [00:09<03:44,  8.60it/s]
+  4%|         | 73/2000 [00:09<03:46,  8.52it/s]
+  4%|         | 74/2000 [00:09<03:44,  8.57it/s]
+  4%|         | 75/2000 [00:09<03:42,  8.65it/s]
+  4%|         | 76/2000 [00:09<03:41,  8.68it/s]
+  4%|         | 77/2000 [00:10<03:41,  8.69it/s]
+  4%|         | 78/2000 [00:10<03:38,  8.80it/s]
+  4%|         | 79/2000 [00:10<03:39,  8.77it/s]
+  4%|         | 80/2000 [00:10<03:38,  8.79it/s]
+  4%|         | 81/2000 [00:10<03:39,  8.76it/s]
+  4%|         | 82/2000 [00:10<03:38,  8.76it/s]
+  4%|         | 83/2000 [00:10<03:39,  8.73it/s]
+  4%|         | 84/2000 [00:10<03:43,  8.59it/s]
+  4%|         | 85/2000 [00:11<03:41,  8.66it/s]
+  4%|         | 86/2000 [00:11<03:38,  8.76it/s]
+  4%|         | 87/2000 [00:11<03:38,  8.76it/s]
+  4%|         | 88/2000 [00:11<03:40,  8.67it/s]
+  4%|         | 89/2000 [00:11<03:45,  8.47it/s]
+  4%|         | 90/2000 [00:11<03:45,  8.46it/s]
+  5%|         | 91/2000 [00:11<03:43,  8.53it/s]
+  5%|         | 92/2000 [00:11<03:43,  8.53it/s]
+  5%|         | 93/2000 [00:11<03:46,  8.42it/s]
+  5%|         | 94/2000 [00:12<03:48,  8.36it/s]
+  5%|         | 95/2000 [00:12<03:46,  8.41it/s]
+  5%|         | 96/2000 [00:12<03:50,  8.28it/s]
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+ 15%|        | 299/2000 [00:41<03:21,  8.44it/s]
+ 15%|        | 299/2000 [00:42<04:03,  6.99it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[survival_function-imbalanced_censoring-True] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.00it/s]
+  3%|         | 3/100 [00:00<00:08, 11.18it/s]
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+  7%|         | 7/100 [00:00<00:07, 12.64it/s]
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+ 91%| | 91/100 [00:08<00:00, 12.64it/s]
+ 93%|| 93/100 [00:08<00:00, 12.77it/s]
+ 95%|| 95/100 [00:08<00:00, 12.57it/s]
+ 97%|| 97/100 [00:09<00:00, 12.54it/s]
+ 99%|| 99/100 [00:09<00:00, 12.40it/s]
+100%|| 100/100 [00:10<00:00,  9.62it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[survival_function-imbalanced_censoring-False] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.20it/s]
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+ 29%|       | 29/100 [00:02<00:05, 13.35it/s]
+ 31%|       | 31/100 [00:02<00:05, 13.41it/s]
+ 33%|      | 33/100 [00:02<00:04, 13.41it/s]
+ 35%|      | 35/100 [00:02<00:04, 13.41it/s]
+ 37%|      | 37/100 [00:02<00:04, 13.34it/s]
+ 39%|      | 39/100 [00:02<00:04, 13.62it/s]
+ 41%|      | 41/100 [00:03<00:04, 13.25it/s]
+ 43%|     | 43/100 [00:03<00:04, 13.52it/s]
+ 45%|     | 45/100 [00:03<00:04, 13.46it/s]
+ 47%|     | 47/100 [00:03<00:04, 13.24it/s]
+ 49%|     | 49/100 [00:03<00:03, 13.36it/s]
+ 51%|     | 51/100 [00:05<00:12,  4.02it/s]
+ 53%|    | 53/100 [00:05<00:09,  5.08it/s]
+ 55%|    | 55/100 [00:05<00:07,  6.26it/s]
+ 57%|    | 57/100 [00:05<00:05,  7.44it/s]
+ 59%|    | 59/100 [00:05<00:04,  8.57it/s]
+ 61%|    | 61/100 [00:05<00:04,  9.69it/s]
+ 63%|   | 63/100 [00:05<00:03, 10.77it/s]
+ 65%|   | 65/100 [00:06<00:03, 11.50it/s]
+ 67%|   | 67/100 [00:06<00:02, 11.86it/s]
+ 69%|   | 69/100 [00:06<00:02, 12.36it/s]
+ 71%|   | 71/100 [00:06<00:02, 12.41it/s]
+ 73%|  | 73/100 [00:06<00:02, 12.35it/s]
+ 75%|  | 75/100 [00:06<00:01, 12.64it/s]
+ 77%|  | 77/100 [00:06<00:01, 13.11it/s]
+ 79%|  | 79/100 [00:07<00:01, 13.46it/s]
+ 81%|  | 81/100 [00:07<00:01, 13.21it/s]
+ 83%| | 83/100 [00:07<00:01, 13.23it/s]
+ 85%| | 85/100 [00:07<00:01, 13.34it/s]
+ 87%| | 87/100 [00:07<00:00, 13.22it/s]
+ 89%| | 89/100 [00:07<00:00, 13.31it/s]
+ 91%| | 91/100 [00:08<00:00, 13.14it/s]
+ 93%|| 93/100 [00:08<00:00, 13.27it/s]
+ 95%|| 95/100 [00:08<00:00, 13.33it/s]
+ 97%|| 97/100 [00:08<00:00, 12.96it/s]
+ 99%|| 99/100 [00:08<00:00, 13.04it/s]
+100%|| 100/100 [00:09<00:00, 10.21it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[survival_function-imbalanced_time_censoring-True] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.05it/s]
+  3%|         | 3/100 [00:00<00:08, 10.99it/s]
+  5%|         | 5/100 [00:00<00:08, 11.79it/s]
+  7%|         | 7/100 [00:00<00:07, 12.13it/s]
+  9%|         | 9/100 [00:00<00:07, 12.44it/s]
+ 11%|         | 11/100 [00:00<00:07, 12.44it/s]
+ 13%|        | 13/100 [00:01<00:07, 12.34it/s]
+ 15%|        | 15/100 [00:01<00:06, 12.66it/s]
+ 17%|        | 17/100 [00:01<00:06, 12.20it/s]
+ 19%|        | 19/100 [00:01<00:06, 12.52it/s]
+ 21%|        | 21/100 [00:01<00:06, 13.00it/s]
+ 23%|       | 23/100 [00:01<00:05, 12.89it/s]
+ 25%|       | 25/100 [00:02<00:05, 12.73it/s]
+ 27%|       | 27/100 [00:02<00:05, 12.87it/s]
+ 29%|       | 29/100 [00:02<00:05, 12.71it/s]
+ 31%|       | 31/100 [00:02<00:05, 12.76it/s]
+ 33%|      | 33/100 [00:02<00:05, 12.56it/s]
+ 35%|      | 35/100 [00:02<00:05, 12.62it/s]
+ 37%|      | 37/100 [00:02<00:05, 12.52it/s]
+ 39%|      | 39/100 [00:03<00:04, 12.72it/s]
+ 41%|      | 41/100 [00:03<00:04, 12.36it/s]
+ 43%|     | 43/100 [00:03<00:04, 12.52it/s]
+ 45%|     | 45/100 [00:03<00:04, 12.30it/s]
+ 47%|     | 47/100 [00:03<00:04, 12.19it/s]
+ 49%|     | 49/100 [00:03<00:04, 12.19it/s]
+ 51%|     | 51/100 [00:05<00:12,  4.05it/s]
+ 53%|    | 53/100 [00:05<00:09,  5.13it/s]
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+ 59%|    | 59/100 [00:05<00:04,  8.57it/s]
+ 61%|    | 61/100 [00:05<00:04,  9.46it/s]
+ 63%|   | 63/100 [00:06<00:03, 10.40it/s]
+ 65%|   | 65/100 [00:06<00:03, 11.03it/s]
+ 67%|   | 67/100 [00:06<00:02, 11.58it/s]
+ 69%|   | 69/100 [00:06<00:02, 12.00it/s]
+ 71%|   | 71/100 [00:06<00:02, 11.89it/s]
+ 73%|  | 73/100 [00:06<00:02, 12.09it/s]
+ 75%|  | 75/100 [00:07<00:02, 12.41it/s]
+ 77%|  | 77/100 [00:07<00:01, 12.48it/s]
+ 79%|  | 79/100 [00:07<00:01, 12.67it/s]
+ 81%|  | 81/100 [00:07<00:01, 12.67it/s]
+ 83%| | 83/100 [00:07<00:01, 12.18it/s]
+ 85%| | 85/100 [00:07<00:01, 12.34it/s]
+ 87%| | 87/100 [00:08<00:01, 12.48it/s]
+ 89%| | 89/100 [00:08<00:00, 12.74it/s]
+ 91%| | 91/100 [00:08<00:00, 12.51it/s]
+ 93%|| 93/100 [00:08<00:00, 12.75it/s]
+ 95%|| 95/100 [00:08<00:00, 12.74it/s]
+ 97%|| 97/100 [00:08<00:00, 12.50it/s]
+ 99%|| 99/100 [00:08<00:00, 12.54it/s]
+100%|| 100/100 [00:10<00:00,  9.90it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[survival_function-imbalanced_time_censoring-False] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  2%|         | 2/100 [00:00<00:08, 11.71it/s]
+  4%|         | 4/100 [00:00<00:07, 12.81it/s]
+  6%|         | 6/100 [00:00<00:07, 12.73it/s]
+  8%|         | 8/100 [00:00<00:07, 13.01it/s]
+ 10%|         | 10/100 [00:00<00:06, 13.36it/s]
+ 12%|        | 12/100 [00:00<00:06, 13.23it/s]
+ 14%|        | 14/100 [00:01<00:06, 13.24it/s]
+ 16%|        | 16/100 [00:01<00:06, 13.45it/s]
+ 18%|        | 18/100 [00:01<00:06, 13.28it/s]
+ 20%|        | 20/100 [00:01<00:06, 13.07it/s]
+ 22%|       | 22/100 [00:01<00:06, 12.93it/s]
+ 24%|       | 24/100 [00:01<00:05, 12.99it/s]
+ 26%|       | 26/100 [00:01<00:05, 12.92it/s]
+ 28%|       | 28/100 [00:02<00:05, 12.76it/s]
+ 30%|       | 30/100 [00:02<00:05, 12.94it/s]
+ 32%|      | 32/100 [00:02<00:05, 13.14it/s]
+ 34%|      | 34/100 [00:02<00:05, 13.17it/s]
+ 36%|      | 36/100 [00:02<00:04, 13.48it/s]
+ 38%|      | 38/100 [00:02<00:04, 13.60it/s]
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+ 42%|     | 42/100 [00:03<00:04, 13.40it/s]
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+ 46%|     | 46/100 [00:03<00:04, 13.37it/s]
+ 48%|     | 48/100 [00:03<00:03, 13.56it/s]
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+ 58%|    | 58/100 [00:05<00:04,  8.69it/s]
+ 60%|    | 60/100 [00:05<00:04,  9.56it/s]
+ 62%|   | 62/100 [00:05<00:03, 10.19it/s]
+ 64%|   | 64/100 [00:05<00:03, 11.04it/s]
+ 66%|   | 66/100 [00:06<00:02, 11.59it/s]
+ 68%|   | 68/100 [00:06<00:02, 12.03it/s]
+ 70%|   | 70/100 [00:06<00:02, 12.09it/s]
+ 72%|  | 72/100 [00:06<00:02, 12.36it/s]
+ 74%|  | 74/100 [00:06<00:02, 12.47it/s]
+ 76%|  | 76/100 [00:06<00:01, 12.69it/s]
+ 78%|  | 78/100 [00:07<00:01, 12.95it/s]
+ 80%|  | 80/100 [00:07<00:01, 13.00it/s]
+ 82%| | 82/100 [00:07<00:01, 13.15it/s]
+ 84%| | 84/100 [00:07<00:01, 13.30it/s]
+ 86%| | 86/100 [00:07<00:01, 13.45it/s]
+ 88%| | 88/100 [00:07<00:00, 13.19it/s]
+ 90%| | 90/100 [00:07<00:00, 12.87it/s]
+ 92%|| 92/100 [00:08<00:00, 12.89it/s]
+ 94%|| 94/100 [00:08<00:00, 12.90it/s]
+ 96%|| 96/100 [00:08<00:00, 13.06it/s]
+ 98%|| 98/100 [00:08<00:00, 13.12it/s]
+100%|| 100/100 [00:09<00:00,  4.09it/s]
+100%|| 100/100 [00:09<00:00, 10.14it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[survival_function-none-True] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.62it/s]
+  3%|         | 3/100 [00:00<00:08, 12.03it/s]
+  5%|         | 5/100 [00:00<00:07, 12.83it/s]
+  7%|         | 7/100 [00:00<00:07, 13.00it/s]
+  9%|         | 9/100 [00:00<00:07, 12.65it/s]
+ 11%|         | 11/100 [00:00<00:06, 12.88it/s]
+ 13%|        | 13/100 [00:01<00:06, 12.75it/s]
+ 15%|        | 15/100 [00:01<00:06, 12.89it/s]
+ 17%|        | 17/100 [00:01<00:06, 12.62it/s]
+ 19%|        | 19/100 [00:01<00:06, 12.84it/s]
+ 21%|        | 21/100 [00:01<00:06, 12.94it/s]
+ 23%|       | 23/100 [00:01<00:05, 13.09it/s]
+ 25%|       | 25/100 [00:01<00:05, 13.08it/s]
+ 27%|       | 27/100 [00:02<00:05, 13.10it/s]
+ 29%|       | 29/100 [00:02<00:05, 13.20it/s]
+ 31%|       | 31/100 [00:02<00:05, 13.08it/s]
+ 33%|      | 33/100 [00:02<00:05, 13.19it/s]
+ 35%|      | 35/100 [00:02<00:04, 13.20it/s]
+ 37%|      | 37/100 [00:02<00:04, 13.16it/s]
+ 39%|      | 39/100 [00:03<00:04, 13.16it/s]
+ 41%|      | 41/100 [00:03<00:04, 13.21it/s]
+ 43%|     | 43/100 [00:03<00:04, 12.96it/s]
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+ 47%|     | 47/100 [00:03<00:04, 13.13it/s]
+ 49%|     | 49/100 [00:03<00:03, 13.23it/s]
+ 51%|     | 51/100 [00:05<00:12,  3.86it/s]
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+ 54%|    | 54/100 [00:05<00:08,  5.17it/s]
+ 55%|    | 55/100 [00:05<00:07,  5.68it/s]
+ 56%|    | 56/100 [00:05<00:07,  6.26it/s]
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+ 59%|    | 59/100 [00:05<00:05,  8.09it/s]
+ 61%|    | 61/100 [00:06<00:04,  9.11it/s]
+ 63%|   | 63/100 [00:06<00:03, 10.13it/s]
+ 65%|   | 65/100 [00:06<00:03, 10.67it/s]
+ 67%|   | 67/100 [00:06<00:02, 11.38it/s]
+ 69%|   | 69/100 [00:06<00:02, 11.99it/s]
+ 71%|   | 71/100 [00:06<00:02, 12.25it/s]
+ 73%|  | 73/100 [00:07<00:02, 12.42it/s]
+ 75%|  | 75/100 [00:07<00:01, 12.82it/s]
+ 77%|  | 77/100 [00:07<00:01, 12.75it/s]
+ 79%|  | 79/100 [00:07<00:01, 12.65it/s]
+ 81%|  | 81/100 [00:07<00:01, 12.20it/s]
+ 83%| | 83/100 [00:07<00:01, 12.61it/s]
+ 85%| | 85/100 [00:07<00:01, 12.63it/s]
+ 87%| | 87/100 [00:08<00:01, 12.85it/s]
+ 89%| | 89/100 [00:08<00:00, 13.07it/s]
+ 91%| | 91/100 [00:08<00:00, 13.17it/s]
+ 93%|| 93/100 [00:08<00:00, 13.38it/s]
+ 95%|| 95/100 [00:08<00:00, 13.34it/s]
+ 97%|| 97/100 [00:08<00:00, 13.24it/s]
+ 99%|| 99/100 [00:09<00:00, 13.17it/s]
+100%|| 100/100 [00:10<00:00,  9.79it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[survival_function-none-False] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.09it/s]
+  3%|         | 3/100 [00:00<00:08, 11.20it/s]
+  5%|         | 5/100 [00:00<00:08, 11.47it/s]
+  7%|         | 7/100 [00:00<00:07, 11.80it/s]
+  9%|         | 9/100 [00:00<00:07, 11.73it/s]
+ 11%|         | 11/100 [00:00<00:07, 11.79it/s]
+ 13%|        | 13/100 [00:01<00:07, 11.72it/s]
+ 15%|        | 15/100 [00:01<00:07, 11.93it/s]
+ 17%|        | 17/100 [00:01<00:06, 12.13it/s]
+ 19%|        | 19/100 [00:01<00:06, 12.22it/s]
+ 21%|        | 21/100 [00:01<00:06, 12.23it/s]
+ 23%|       | 23/100 [00:01<00:06, 12.36it/s]
+ 25%|       | 25/100 [00:02<00:06, 12.31it/s]
+ 27%|       | 27/100 [00:02<00:06, 12.14it/s]
+ 29%|       | 29/100 [00:02<00:05, 11.96it/s]
+ 31%|       | 31/100 [00:02<00:05, 11.78it/s]
+ 33%|      | 33/100 [00:02<00:05, 11.89it/s]
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+ 59%|    | 59/100 [00:06<00:05,  7.99it/s]
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+ 63%|   | 63/100 [00:06<00:03,  9.76it/s]
+ 65%|   | 65/100 [00:06<00:03, 10.29it/s]
+ 67%|   | 67/100 [00:06<00:03, 10.58it/s]
+ 69%|   | 69/100 [00:06<00:02, 10.92it/s]
+ 71%|   | 71/100 [00:07<00:02, 11.39it/s]
+ 73%|  | 73/100 [00:07<00:02, 11.65it/s]
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+ 79%|  | 79/100 [00:07<00:01, 11.72it/s]
+ 81%|  | 81/100 [00:07<00:01, 11.87it/s]
+ 83%| | 83/100 [00:08<00:01, 11.93it/s]
+ 85%| | 85/100 [00:08<00:01, 11.73it/s]
+ 87%| | 87/100 [00:08<00:01, 11.65it/s]
+ 89%| | 89/100 [00:08<00:00, 11.70it/s]
+ 91%| | 91/100 [00:08<00:00, 11.80it/s]
+ 93%|| 93/100 [00:08<00:00, 11.84it/s]
+ 95%|| 95/100 [00:09<00:00, 11.98it/s]
+ 97%|| 97/100 [00:09<00:00, 11.99it/s]
+ 99%|| 99/100 [00:09<00:00, 12.06it/s]
+100%|| 100/100 [00:10<00:00,  9.49it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[uncensoring-imbalanced_censoring-True] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  2%|         | 2/100 [00:00<00:08, 11.43it/s]
+  4%|         | 4/100 [00:00<00:07, 12.53it/s]
+  6%|         | 6/100 [00:00<00:07, 12.81it/s]
+  8%|         | 8/100 [00:00<00:07, 12.78it/s]
+ 10%|         | 10/100 [00:00<00:06, 12.93it/s]
+ 12%|        | 12/100 [00:00<00:06, 13.02it/s]
+ 14%|        | 14/100 [00:01<00:06, 12.94it/s]
+ 16%|        | 16/100 [00:01<00:06, 13.21it/s]
+ 18%|        | 18/100 [00:01<00:06, 13.16it/s]
+ 20%|        | 20/100 [00:01<00:06, 12.80it/s]
+ 22%|       | 22/100 [00:01<00:05, 13.12it/s]
+ 24%|       | 24/100 [00:01<00:05, 13.13it/s]
+ 26%|       | 26/100 [00:02<00:05, 12.92it/s]
+ 28%|       | 28/100 [00:02<00:05, 12.82it/s]
+ 30%|       | 30/100 [00:02<00:05, 12.86it/s]
+ 32%|      | 32/100 [00:02<00:05, 13.05it/s]
+ 34%|      | 34/100 [00:02<00:05, 13.07it/s]
+ 36%|      | 36/100 [00:02<00:05, 12.59it/s]
+ 38%|      | 38/100 [00:02<00:04, 12.85it/s]
+ 40%|      | 40/100 [00:03<00:04, 13.07it/s]
+ 42%|     | 42/100 [00:03<00:04, 12.79it/s]
+ 44%|     | 44/100 [00:03<00:04, 12.66it/s]
+ 46%|     | 46/100 [00:03<00:04, 12.65it/s]
+ 48%|     | 48/100 [00:03<00:04, 12.63it/s]
+ 50%|     | 50/100 [00:05<00:12,  4.02it/s]
+ 51%|     | 51/100 [00:05<00:10,  4.49it/s]
+ 53%|    | 53/100 [00:05<00:08,  5.75it/s]
+ 55%|    | 55/100 [00:05<00:06,  7.03it/s]
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+ 59%|    | 59/100 [00:05<00:04,  9.19it/s]
+ 61%|    | 61/100 [00:05<00:03, 10.06it/s]
+ 63%|   | 63/100 [00:06<00:03, 10.89it/s]
+ 65%|   | 65/100 [00:06<00:03, 11.51it/s]
+ 67%|   | 67/100 [00:06<00:02, 11.75it/s]
+ 69%|   | 69/100 [00:06<00:02, 11.95it/s]
+ 71%|   | 71/100 [00:06<00:02, 12.07it/s]
+ 73%|  | 73/100 [00:06<00:02, 12.35it/s]
+ 75%|  | 75/100 [00:07<00:02, 12.46it/s]
+ 77%|  | 77/100 [00:07<00:01, 12.61it/s]
+ 79%|  | 79/100 [00:07<00:01, 12.58it/s]
+ 81%|  | 81/100 [00:07<00:01, 12.60it/s]
+ 83%| | 83/100 [00:07<00:01, 12.61it/s]
+ 85%| | 85/100 [00:07<00:01, 12.78it/s]
+ 87%| | 87/100 [00:07<00:01, 12.55it/s]
+ 89%| | 89/100 [00:08<00:00, 12.69it/s]
+ 91%| | 91/100 [00:08<00:00, 12.79it/s]
+ 93%|| 93/100 [00:08<00:00, 12.88it/s]
+ 95%|| 95/100 [00:08<00:00, 12.93it/s]
+ 97%|| 97/100 [00:08<00:00, 12.74it/s]
+ 99%|| 99/100 [00:08<00:00, 12.54it/s]
+100%|| 100/100 [00:09<00:00, 10.00it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[uncensoring-imbalanced_censoring-False] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.60it/s]
+  3%|         | 3/100 [00:00<00:07, 12.84it/s]
+  5%|         | 5/100 [00:00<00:06, 13.69it/s]
+  7%|         | 7/100 [00:00<00:06, 13.77it/s]
+  9%|         | 9/100 [00:00<00:06, 13.84it/s]
+ 11%|         | 11/100 [00:00<00:06, 13.77it/s]
+ 13%|        | 13/100 [00:00<00:06, 13.66it/s]
+ 15%|        | 15/100 [00:01<00:06, 13.88it/s]
+ 17%|        | 17/100 [00:01<00:05, 14.03it/s]
+ 19%|        | 19/100 [00:01<00:05, 13.78it/s]
+ 21%|        | 21/100 [00:01<00:05, 13.89it/s]
+ 23%|       | 23/100 [00:01<00:05, 13.66it/s]
+ 25%|       | 25/100 [00:01<00:05, 13.56it/s]
+ 27%|       | 27/100 [00:01<00:05, 13.64it/s]
+ 29%|       | 29/100 [00:02<00:05, 13.58it/s]
+ 31%|       | 31/100 [00:02<00:05, 13.66it/s]
+ 33%|      | 33/100 [00:02<00:04, 13.86it/s]
+ 35%|      | 35/100 [00:02<00:04, 13.63it/s]
+ 37%|      | 37/100 [00:02<00:04, 13.83it/s]
+ 39%|      | 39/100 [00:02<00:04, 13.79it/s]
+ 41%|      | 41/100 [00:03<00:04, 13.54it/s]
+ 43%|     | 43/100 [00:03<00:04, 13.69it/s]
+ 45%|     | 45/100 [00:03<00:03, 13.93it/s]
+ 47%|     | 47/100 [00:03<00:03, 13.60it/s]
+ 49%|     | 49/100 [00:03<00:03, 13.47it/s]
+ 51%|     | 51/100 [00:04<00:12,  3.86it/s]
+ 53%|    | 53/100 [00:05<00:09,  4.90it/s]
+ 55%|    | 55/100 [00:05<00:07,  6.13it/s]
+ 57%|    | 57/100 [00:05<00:05,  7.29it/s]
+ 59%|    | 59/100 [00:05<00:04,  8.36it/s]
+ 61%|    | 61/100 [00:05<00:04,  9.41it/s]
+ 63%|   | 63/100 [00:05<00:03, 10.21it/s]
+ 65%|   | 65/100 [00:06<00:03, 11.03it/s]
+ 67%|   | 67/100 [00:06<00:02, 11.61it/s]
+ 69%|   | 69/100 [00:06<00:02, 12.10it/s]
+ 71%|   | 71/100 [00:06<00:02, 12.61it/s]
+ 73%|  | 73/100 [00:06<00:02, 12.77it/s]
+ 75%|  | 75/100 [00:06<00:01, 13.20it/s]
+ 77%|  | 77/100 [00:06<00:01, 13.25it/s]
+ 79%|  | 79/100 [00:07<00:01, 13.13it/s]
+ 81%|  | 81/100 [00:07<00:01, 13.32it/s]
+ 83%| | 83/100 [00:07<00:01, 13.52it/s]
+ 85%| | 85/100 [00:07<00:01, 13.58it/s]
+ 87%| | 87/100 [00:07<00:00, 13.51it/s]
+ 89%| | 89/100 [00:07<00:00, 13.59it/s]
+ 91%| | 91/100 [00:07<00:00, 13.67it/s]
+ 93%|| 93/100 [00:08<00:00, 13.46it/s]
+ 95%|| 95/100 [00:08<00:00, 13.60it/s]
+ 97%|| 97/100 [00:08<00:00, 13.53it/s]
+ 99%|| 99/100 [00:08<00:00, 13.26it/s]
+100%|| 100/100 [00:09<00:00, 10.36it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[uncensoring-imbalanced_time_censoring-True] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.40it/s]
+  3%|         | 3/100 [00:00<00:08, 11.85it/s]
+  5%|         | 5/100 [00:00<00:07, 12.80it/s]
+  7%|         | 7/100 [00:00<00:07, 12.84it/s]
+  9%|         | 9/100 [00:00<00:06, 13.18it/s]
+ 11%|         | 11/100 [00:00<00:06, 12.96it/s]
+ 13%|        | 13/100 [00:01<00:06, 12.72it/s]
+ 15%|        | 15/100 [00:01<00:06, 12.71it/s]
+ 17%|        | 17/100 [00:01<00:06, 12.87it/s]
+ 19%|        | 19/100 [00:01<00:06, 12.81it/s]
+ 21%|        | 21/100 [00:01<00:06, 13.05it/s]
+ 23%|       | 23/100 [00:01<00:05, 13.25it/s]
+ 25%|       | 25/100 [00:01<00:05, 13.08it/s]
+ 27%|       | 27/100 [00:02<00:05, 13.07it/s]
+ 29%|       | 29/100 [00:02<00:05, 13.24it/s]
+ 31%|       | 31/100 [00:02<00:05, 13.07it/s]
+ 33%|      | 33/100 [00:02<00:05, 13.37it/s]
+ 35%|      | 35/100 [00:02<00:04, 13.52it/s]
+ 37%|      | 37/100 [00:02<00:04, 13.38it/s]
+ 39%|      | 39/100 [00:02<00:04, 13.39it/s]
+ 41%|      | 41/100 [00:03<00:04, 13.43it/s]
+ 43%|     | 43/100 [00:03<00:04, 13.22it/s]
+ 45%|     | 45/100 [00:03<00:04, 13.30it/s]
+ 47%|     | 47/100 [00:03<00:04, 12.79it/s]
+ 49%|     | 49/100 [00:03<00:03, 12.94it/s]
+ 51%|     | 51/100 [00:05<00:13,  3.75it/s]
+ 53%|    | 53/100 [00:05<00:10,  4.69it/s]
+ 55%|    | 55/100 [00:05<00:07,  5.85it/s]
+ 57%|    | 57/100 [00:05<00:06,  6.86it/s]
+ 59%|    | 59/100 [00:05<00:05,  8.06it/s]
+ 61%|    | 61/100 [00:05<00:04,  9.17it/s]
+ 63%|   | 63/100 [00:06<00:03,  9.92it/s]
+ 65%|   | 65/100 [00:06<00:03, 10.62it/s]
+ 67%|   | 67/100 [00:06<00:02, 11.24it/s]
+ 69%|   | 69/100 [00:06<00:02, 11.84it/s]
+ 71%|   | 71/100 [00:06<00:02, 12.14it/s]
+ 73%|  | 73/100 [00:06<00:02, 12.33it/s]
+ 75%|  | 75/100 [00:07<00:02, 12.42it/s]
+ 77%|  | 77/100 [00:07<00:01, 12.76it/s]
+ 79%|  | 79/100 [00:07<00:01, 13.04it/s]
+ 81%|  | 81/100 [00:07<00:01, 13.20it/s]
+ 83%| | 83/100 [00:07<00:01, 13.09it/s]
+ 85%| | 85/100 [00:07<00:01, 12.81it/s]
+ 87%| | 87/100 [00:07<00:01, 12.90it/s]
+ 89%| | 89/100 [00:08<00:00, 13.09it/s]
+ 91%| | 91/100 [00:08<00:00, 13.05it/s]
+ 93%|| 93/100 [00:08<00:00, 13.27it/s]
+ 95%|| 95/100 [00:08<00:00, 13.25it/s]
+ 97%|| 97/100 [00:08<00:00, 13.25it/s]
+ 99%|| 99/100 [00:08<00:00, 13.34it/s]
+100%|| 100/100 [00:10<00:00,  9.99it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[uncensoring-imbalanced_time_censoring-False] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.50it/s]
+  3%|         | 3/100 [00:00<00:08, 12.11it/s]
+  5%|         | 5/100 [00:00<00:07, 12.83it/s]
+  7%|         | 7/100 [00:00<00:07, 12.73it/s]
+  9%|         | 9/100 [00:00<00:06, 13.25it/s]
+ 11%|         | 11/100 [00:00<00:06, 12.92it/s]
+ 13%|        | 13/100 [00:01<00:06, 13.36it/s]
+ 15%|        | 15/100 [00:01<00:06, 13.47it/s]
+ 17%|        | 17/100 [00:01<00:06, 13.44it/s]
+ 19%|        | 19/100 [00:01<00:06, 13.44it/s]
+ 21%|        | 21/100 [00:01<00:05, 13.32it/s]
+ 23%|       | 23/100 [00:01<00:05, 13.34it/s]
+ 25%|       | 25/100 [00:01<00:05, 13.53it/s]
+ 27%|       | 27/100 [00:02<00:05, 13.69it/s]
+ 29%|       | 29/100 [00:02<00:05, 13.81it/s]
+ 31%|       | 31/100 [00:02<00:04, 13.87it/s]
+ 33%|      | 33/100 [00:02<00:04, 13.62it/s]
+ 35%|      | 35/100 [00:02<00:04, 13.58it/s]
+ 37%|      | 37/100 [00:02<00:04, 13.00it/s]
+ 39%|      | 39/100 [00:02<00:04, 13.10it/s]
+ 41%|      | 41/100 [00:03<00:04, 12.95it/s]
+ 43%|     | 43/100 [00:03<00:04, 13.17it/s]
+ 45%|     | 45/100 [00:03<00:04, 13.33it/s]
+ 47%|     | 47/100 [00:03<00:03, 13.42it/s]
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+ 53%|    | 53/100 [00:05<00:09,  4.70it/s]
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+ 57%|    | 57/100 [00:05<00:06,  7.08it/s]
+ 59%|    | 59/100 [00:05<00:04,  8.33it/s]
+ 61%|    | 61/100 [00:05<00:04,  9.45it/s]
+ 63%|   | 63/100 [00:06<00:03, 10.52it/s]
+ 65%|   | 65/100 [00:06<00:03, 11.43it/s]
+ 67%|   | 67/100 [00:06<00:02, 12.00it/s]
+ 69%|   | 69/100 [00:06<00:02, 12.54it/s]
+ 71%|   | 71/100 [00:06<00:02, 12.79it/s]
+ 73%|  | 73/100 [00:06<00:02, 13.05it/s]
+ 75%|  | 75/100 [00:06<00:01, 13.06it/s]
+ 77%|  | 77/100 [00:07<00:01, 13.05it/s]
+ 79%|  | 79/100 [00:07<00:01, 13.13it/s]
+ 81%|  | 81/100 [00:07<00:01, 13.30it/s]
+ 83%| | 83/100 [00:07<00:01, 13.37it/s]
+ 85%| | 85/100 [00:07<00:01, 13.02it/s]
+ 87%| | 87/100 [00:07<00:00, 13.27it/s]
+ 89%| | 89/100 [00:07<00:00, 13.55it/s]
+ 91%| | 91/100 [00:08<00:00, 13.54it/s]
+ 93%|| 93/100 [00:08<00:00, 13.47it/s]
+ 95%|| 95/100 [00:08<00:00, 13.53it/s]
+ 97%|| 97/100 [00:08<00:00, 13.66it/s]
+ 99%|| 99/100 [00:08<00:00, 13.55it/s]
+100%|| 100/100 [00:09<00:00, 10.22it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[uncensoring-none-True] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:12,  7.75it/s]
+  3%|         | 3/100 [00:00<00:08, 10.96it/s]
+  5%|         | 5/100 [00:00<00:07, 11.89it/s]
+  7%|         | 7/100 [00:00<00:07, 12.56it/s]
+  9%|         | 9/100 [00:00<00:07, 12.42it/s]
+ 11%|         | 11/100 [00:00<00:06, 13.09it/s]
+ 13%|        | 13/100 [00:01<00:06, 12.92it/s]
+ 15%|        | 15/100 [00:01<00:06, 12.82it/s]
+ 17%|        | 17/100 [00:01<00:06, 12.68it/s]
+ 19%|        | 19/100 [00:01<00:06, 13.12it/s]
+ 21%|        | 21/100 [00:01<00:06, 13.08it/s]
+ 23%|       | 23/100 [00:01<00:05, 13.41it/s]
+ 25%|       | 25/100 [00:01<00:05, 13.23it/s]
+ 27%|       | 27/100 [00:02<00:05, 13.30it/s]
+ 29%|       | 29/100 [00:02<00:05, 12.86it/s]
+ 31%|       | 31/100 [00:02<00:05, 12.60it/s]
+ 33%|      | 33/100 [00:02<00:05, 12.79it/s]
+ 35%|      | 35/100 [00:02<00:05, 12.51it/s]
+ 37%|      | 37/100 [00:02<00:04, 12.88it/s]
+ 39%|      | 39/100 [00:03<00:04, 12.95it/s]
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+ 49%|     | 49/100 [00:03<00:03, 13.24it/s]
+ 51%|     | 51/100 [00:05<00:12,  4.04it/s]
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+ 55%|    | 55/100 [00:05<00:07,  6.35it/s]
+ 57%|    | 57/100 [00:05<00:05,  7.57it/s]
+ 59%|    | 59/100 [00:05<00:04,  8.69it/s]
+ 61%|    | 61/100 [00:05<00:04,  9.71it/s]
+ 63%|   | 63/100 [00:05<00:03, 10.55it/s]
+ 65%|   | 65/100 [00:06<00:03, 11.30it/s]
+ 67%|   | 67/100 [00:06<00:02, 11.82it/s]
+ 69%|   | 69/100 [00:06<00:02, 12.00it/s]
+ 71%|   | 71/100 [00:06<00:02, 12.34it/s]
+ 73%|  | 73/100 [00:06<00:02, 12.41it/s]
+ 75%|  | 75/100 [00:06<00:01, 12.61it/s]
+ 77%|  | 77/100 [00:07<00:01, 12.77it/s]
+ 79%|  | 79/100 [00:07<00:01, 12.88it/s]
+ 81%|  | 81/100 [00:07<00:01, 12.80it/s]
+ 83%| | 83/100 [00:07<00:01, 12.89it/s]
+ 85%| | 85/100 [00:07<00:01, 13.09it/s]
+ 87%| | 87/100 [00:07<00:00, 13.08it/s]
+ 89%| | 89/100 [00:07<00:00, 13.12it/s]
+ 91%| | 91/100 [00:08<00:00, 12.94it/s]
+ 93%|| 93/100 [00:08<00:00, 13.17it/s]
+ 95%|| 95/100 [00:08<00:00, 13.40it/s]
+ 97%|| 97/100 [00:08<00:00, 13.44it/s]
+ 99%|| 99/100 [00:08<00:00, 13.57it/s]
+100%|| 100/100 [00:09<00:00, 10.22it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_fit[uncensoring-none-False] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:10,  9.05it/s]
+  3%|         | 3/100 [00:00<00:08, 11.10it/s]
+  5%|         | 5/100 [00:00<00:08, 11.60it/s]
+  7%|         | 7/100 [00:00<00:07, 11.74it/s]
+  9%|         | 9/100 [00:00<00:07, 12.13it/s]
+ 11%|         | 11/100 [00:00<00:07, 12.07it/s]
+ 13%|        | 13/100 [00:01<00:07, 12.17it/s]
+ 15%|        | 15/100 [00:01<00:06, 12.16it/s]
+ 17%|        | 17/100 [00:01<00:06, 12.00it/s]
+ 19%|        | 19/100 [00:01<00:06, 12.17it/s]
+ 21%|        | 21/100 [00:01<00:06, 11.97it/s]
+ 23%|       | 23/100 [00:01<00:06, 11.84it/s]
+ 25%|       | 25/100 [00:02<00:06, 11.90it/s]
+ 27%|       | 27/100 [00:02<00:06, 11.74it/s]
+ 29%|       | 29/100 [00:02<00:06, 11.75it/s]
+ 31%|       | 31/100 [00:02<00:05, 11.64it/s]
+ 33%|      | 33/100 [00:02<00:05, 11.86it/s]
+ 35%|      | 35/100 [00:02<00:05, 11.91it/s]
+ 37%|      | 37/100 [00:03<00:05, 12.03it/s]
+ 39%|      | 39/100 [00:03<00:05, 12.19it/s]
+ 41%|      | 41/100 [00:03<00:04, 12.23it/s]
+ 43%|     | 43/100 [00:03<00:04, 12.45it/s]
+ 45%|     | 45/100 [00:03<00:04, 12.23it/s]
+ 47%|     | 47/100 [00:03<00:04, 12.32it/s]
+ 49%|     | 49/100 [00:04<00:04, 12.12it/s]
+ 51%|     | 51/100 [00:05<00:12,  4.05it/s]
+ 53%|    | 53/100 [00:05<00:09,  5.07it/s]
+ 55%|    | 55/100 [00:05<00:07,  6.17it/s]
+ 57%|    | 57/100 [00:05<00:05,  7.29it/s]
+ 59%|    | 59/100 [00:05<00:04,  8.25it/s]
+ 61%|    | 61/100 [00:06<00:04,  9.22it/s]
+ 63%|   | 63/100 [00:06<00:03, 10.14it/s]
+ 65%|   | 65/100 [00:06<00:03, 10.82it/s]
+ 67%|   | 67/100 [00:06<00:03, 10.93it/s]
+ 69%|   | 69/100 [00:06<00:02, 11.35it/s]
+ 71%|   | 71/100 [00:06<00:02, 11.70it/s]
+ 73%|  | 73/100 [00:07<00:02, 11.81it/s]
+ 75%|  | 75/100 [00:07<00:02, 12.11it/s]
+ 77%|  | 77/100 [00:07<00:01, 12.27it/s]
+ 79%|  | 79/100 [00:07<00:01, 12.01it/s]
+ 81%|  | 81/100 [00:07<00:01, 12.22it/s]
+ 83%| | 83/100 [00:07<00:01, 12.15it/s]
+ 85%| | 85/100 [00:08<00:01, 12.37it/s]
+ 87%| | 87/100 [00:08<00:01, 12.55it/s]
+ 89%| | 89/100 [00:08<00:00, 12.40it/s]
+ 91%| | 91/100 [00:08<00:00, 12.14it/s]
+ 93%|| 93/100 [00:08<00:00, 12.26it/s]
+ 95%|| 95/100 [00:08<00:00, 11.90it/s]
+ 97%|| 97/100 [00:09<00:00, 12.03it/s]
+ 99%|| 99/100 [00:09<00:00, 11.86it/s]
+100%|| 100/100 [00:10<00:00,  9.63it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_generate[uncensoring] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:12,  8.05it/s]
+  3%|         | 3/100 [00:00<00:08, 11.33it/s]
+  5%|         | 5/100 [00:00<00:07, 12.31it/s]
+  7%|         | 7/100 [00:00<00:07, 12.37it/s]
+  9%|         | 9/100 [00:00<00:07, 12.58it/s]
+ 11%|         | 11/100 [00:00<00:07, 12.59it/s]
+ 13%|        | 13/100 [00:01<00:06, 12.90it/s]
+ 15%|        | 15/100 [00:01<00:06, 13.00it/s]
+ 17%|        | 17/100 [00:01<00:06, 13.02it/s]
+ 19%|        | 19/100 [00:01<00:06, 12.67it/s]
+ 21%|        | 21/100 [00:01<00:06, 12.39it/s]
+ 23%|       | 23/100 [00:01<00:06, 12.63it/s]
+ 25%|       | 25/100 [00:01<00:05, 12.88it/s]
+ 27%|       | 27/100 [00:02<00:05, 13.03it/s]
+ 29%|       | 29/100 [00:02<00:05, 12.88it/s]
+ 31%|       | 31/100 [00:02<00:05, 13.17it/s]
+ 33%|      | 33/100 [00:02<00:05, 13.13it/s]
+ 35%|      | 35/100 [00:02<00:04, 13.35it/s]
+ 37%|      | 37/100 [00:02<00:04, 13.27it/s]
+ 39%|      | 39/100 [00:03<00:04, 12.99it/s]
+ 41%|      | 41/100 [00:03<00:04, 13.06it/s]
+ 43%|     | 43/100 [00:03<00:04, 12.93it/s]
+ 45%|     | 45/100 [00:03<00:04, 13.09it/s]
+ 47%|     | 47/100 [00:03<00:04, 12.84it/s]
+ 49%|     | 49/100 [00:03<00:03, 12.96it/s]
+ 51%|     | 51/100 [00:03<00:03, 12.56it/s]
+ 53%|    | 53/100 [00:04<00:03, 12.93it/s]
+ 55%|    | 55/100 [00:04<00:03, 12.65it/s]
+ 57%|    | 57/100 [00:04<00:03, 12.67it/s]
+ 59%|    | 59/100 [00:04<00:03, 12.84it/s]
+ 61%|    | 61/100 [00:04<00:02, 13.04it/s]
+ 63%|   | 63/100 [00:04<00:02, 13.20it/s]
+ 65%|   | 65/100 [00:05<00:02, 13.00it/s]
+ 67%|   | 67/100 [00:05<00:02, 13.02it/s]
+ 69%|   | 69/100 [00:05<00:02, 12.76it/s]
+ 71%|   | 71/100 [00:05<00:02, 12.71it/s]
+ 73%|  | 73/100 [00:05<00:02, 13.01it/s]
+ 75%|  | 75/100 [00:05<00:01, 13.02it/s]
+ 77%|  | 77/100 [00:05<00:01, 13.32it/s]
+ 79%|  | 79/100 [00:06<00:01, 13.08it/s]
+ 81%|  | 81/100 [00:06<00:01, 12.97it/s]
+ 83%| | 83/100 [00:06<00:01, 12.86it/s]
+ 85%| | 85/100 [00:06<00:01, 12.63it/s]
+ 87%| | 87/100 [00:06<00:01, 12.73it/s]
+ 89%| | 89/100 [00:06<00:00, 12.98it/s]
+ 91%| | 91/100 [00:07<00:00, 12.90it/s]
+ 93%|| 93/100 [00:07<00:00, 12.93it/s]
+ 95%|| 95/100 [00:07<00:00, 12.88it/s]
+ 97%|| 97/100 [00:07<00:00, 12.86it/s]
+ 99%|| 99/100 [00:07<00:00, 12.87it/s]
+100%|| 100/100 [00:08<00:00, 11.22it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_generate[survival_function] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  1%|          | 1/100 [00:00<00:11,  8.58it/s]
+  3%|         | 3/100 [00:00<00:08, 11.06it/s]
+  5%|         | 5/100 [00:00<00:08, 11.64it/s]
+  7%|         | 7/100 [00:00<00:07, 12.23it/s]
+  9%|         | 9/100 [00:00<00:07, 12.36it/s]
+ 11%|         | 11/100 [00:00<00:07, 12.49it/s]
+ 13%|        | 13/100 [00:01<00:06, 12.80it/s]
+ 15%|        | 15/100 [00:01<00:06, 12.96it/s]
+ 17%|        | 17/100 [00:01<00:06, 12.74it/s]
+ 19%|        | 19/100 [00:01<00:06, 12.77it/s]
+ 21%|        | 21/100 [00:01<00:06, 12.78it/s]
+ 23%|       | 23/100 [00:01<00:05, 12.93it/s]
+ 25%|       | 25/100 [00:01<00:05, 12.82it/s]
+ 27%|       | 27/100 [00:02<00:05, 12.55it/s]
+ 29%|       | 29/100 [00:02<00:05, 12.81it/s]
+ 31%|       | 31/100 [00:02<00:05, 12.64it/s]
+ 33%|      | 33/100 [00:02<00:05, 12.65it/s]
+ 35%|      | 35/100 [00:02<00:05, 12.65it/s]
+ 37%|      | 37/100 [00:02<00:04, 12.71it/s]
+ 39%|      | 39/100 [00:03<00:04, 12.87it/s]
+ 41%|      | 41/100 [00:03<00:04, 12.89it/s]
+ 43%|     | 43/100 [00:03<00:04, 12.49it/s]
+ 45%|     | 45/100 [00:03<00:04, 12.53it/s]
+ 47%|     | 47/100 [00:03<00:04, 12.83it/s]
+ 49%|     | 49/100 [00:03<00:03, 12.80it/s]
+ 51%|     | 51/100 [00:04<00:03, 12.28it/s]
+ 53%|    | 53/100 [00:04<00:03, 12.66it/s]
+ 55%|    | 55/100 [00:04<00:03, 12.68it/s]
+ 57%|    | 57/100 [00:04<00:03, 12.85it/s]
+ 59%|    | 59/100 [00:04<00:03, 13.03it/s]
+ 61%|    | 61/100 [00:04<00:03, 12.68it/s]
+ 63%|   | 63/100 [00:04<00:02, 12.82it/s]
+ 65%|   | 65/100 [00:05<00:02, 12.63it/s]
+ 67%|   | 67/100 [00:05<00:02, 12.97it/s]
+ 69%|   | 69/100 [00:05<00:02, 13.01it/s]
+ 71%|   | 71/100 [00:05<00:02, 12.89it/s]
+ 73%|  | 73/100 [00:05<00:02, 12.64it/s]
+ 75%|  | 75/100 [00:05<00:02, 12.45it/s]
+ 77%|  | 77/100 [00:06<00:01, 12.69it/s]
+ 79%|  | 79/100 [00:06<00:01, 12.49it/s]
+ 81%|  | 81/100 [00:06<00:01, 12.26it/s]
+ 83%| | 83/100 [00:06<00:01, 12.22it/s]
+ 85%| | 85/100 [00:06<00:01, 12.58it/s]
+ 87%| | 87/100 [00:06<00:01, 12.60it/s]
+ 89%| | 89/100 [00:07<00:00, 12.48it/s]
+ 91%| | 91/100 [00:07<00:00, 12.32it/s]
+ 93%|| 93/100 [00:07<00:00, 12.35it/s]
+ 95%|| 95/100 [00:07<00:00, 12.34it/s]
+ 97%|| 97/100 [00:07<00:00, 12.29it/s]
+ 99%|| 99/100 [00:07<00:00, 12.55it/s]
+100%|| 100/100 [00:09<00:00, 11.09it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_survival_plugin_generate_constraints[uncensoring] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  2%|         | 2/100 [00:00<00:08, 11.46it/s]
+  4%|         | 4/100 [00:00<00:07, 12.92it/s]
+  6%|         | 6/100 [00:00<00:07, 12.34it/s]
+  8%|         | 8/100 [00:00<00:07, 12.73it/s]
+ 10%|         | 10/100 [00:00<00:06, 12.91it/s]
+ 12%|        | 12/100 [00:00<00:06, 13.07it/s]
+ 14%|        | 14/100 [00:01<00:06, 13.13it/s]
+ 16%|        | 16/100 [00:01<00:06, 13.23it/s]
+ 18%|        | 18/100 [00:01<00:06, 13.44it/s]
+ 20%|        | 20/100 [00:01<00:05, 13.57it/s]
+ 22%|       | 22/100 [00:01<00:05, 13.41it/s]
+ 24%|       | 24/100 [00:01<00:05, 13.33it/s]
+ 26%|       | 26/100 [00:01<00:05, 13.25it/s]
+ 28%|       | 28/100 [00:02<00:05, 13.08it/s]
+ 30%|       | 30/100 [00:02<00:05, 13.04it/s]
+ 32%|      | 32/100 [00:02<00:05, 13.06it/s]
+ 34%|      | 34/100 [00:02<00:05, 13.07it/s]
+ 36%|      | 36/100 [00:02<00:05, 12.61it/s]
+ 38%|      | 38/100 [00:02<00:04, 12.68it/s]
+ 40%|      | 40/100 [00:03<00:04, 12.89it/s]
+ 42%|     | 42/100 [00:03<00:04, 12.50it/s]
+ 44%|     | 44/100 [00:03<00:04, 12.69it/s]
+ 46%|     | 46/100 [00:03<00:04, 12.93it/s]
+ 48%|     | 48/100 [00:03<00:03, 13.08it/s]
+ 50%|     | 50/100 [00:03<00:03, 12.86it/s]
+ 52%|    | 52/100 [00:04<00:03, 12.58it/s]
+ 54%|    | 54/100 [00:04<00:03, 12.72it/s]
+ 56%|    | 56/100 [00:04<00:03, 13.04it/s]
+ 58%|    | 58/100 [00:04<00:03, 13.10it/s]
+ 60%|    | 60/100 [00:04<00:03, 13.24it/s]
+ 62%|   | 62/100 [00:04<00:02, 13.41it/s]
+ 64%|   | 64/100 [00:04<00:02, 13.32it/s]
+ 66%|   | 66/100 [00:05<00:02, 13.02it/s]
+ 68%|   | 68/100 [00:05<00:02, 13.08it/s]
+ 70%|   | 70/100 [00:05<00:02, 13.13it/s]
+ 72%|  | 72/100 [00:05<00:02, 13.06it/s]
+ 74%|  | 74/100 [00:05<00:02, 12.90it/s]
+ 76%|  | 76/100 [00:05<00:01, 12.95it/s]
+ 78%|  | 78/100 [00:06<00:01, 13.02it/s]
+ 80%|  | 80/100 [00:06<00:01, 12.92it/s]
+ 82%| | 82/100 [00:06<00:01, 13.16it/s]
+ 84%| | 84/100 [00:06<00:01, 12.84it/s]
+ 86%| | 86/100 [00:06<00:01, 12.62it/s]
+ 88%| | 88/100 [00:06<00:00, 12.86it/s]
+ 90%| | 90/100 [00:06<00:00, 12.89it/s]
+ 92%|| 92/100 [00:07<00:00, 12.94it/s]
+ 94%|| 94/100 [00:07<00:00, 13.33it/s]
+ 96%|| 96/100 [00:07<00:00, 13.04it/s]
+ 98%|| 98/100 [00:07<00:00, 13.27it/s]
+100%|| 100/100 [00:07<00:00, 13.00it/s]
+100%|| 100/100 [00:07<00:00, 12.99it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_survival_plugin_generate_constraints[survival_function] 
+  0%|          | 0/100 [00:00<?, ?it/s]
+  2%|         | 2/100 [00:00<00:08, 11.38it/s]
+  4%|         | 4/100 [00:00<00:07, 12.22it/s]
+  6%|         | 6/100 [00:00<00:07, 12.72it/s]
+  8%|         | 8/100 [00:00<00:07, 12.77it/s]
+ 10%|         | 10/100 [00:00<00:07, 12.83it/s]
+ 12%|        | 12/100 [00:00<00:06, 13.17it/s]
+ 14%|        | 14/100 [00:01<00:06, 12.89it/s]
+ 16%|        | 16/100 [00:01<00:06, 12.85it/s]
+ 18%|        | 18/100 [00:01<00:06, 12.91it/s]
+ 20%|        | 20/100 [00:01<00:06, 12.61it/s]
+ 22%|       | 22/100 [00:01<00:06, 12.83it/s]
+ 24%|       | 24/100 [00:01<00:05, 12.88it/s]
+ 26%|       | 26/100 [00:02<00:05, 13.07it/s]
+ 28%|       | 28/100 [00:02<00:05, 13.14it/s]
+ 30%|       | 30/100 [00:02<00:05, 13.26it/s]
+ 32%|      | 32/100 [00:02<00:05, 13.13it/s]
+ 34%|      | 34/100 [00:02<00:05, 12.73it/s]
+ 36%|      | 36/100 [00:02<00:05, 12.56it/s]
+ 38%|      | 38/100 [00:02<00:04, 12.43it/s]
+ 40%|      | 40/100 [00:03<00:04, 12.63it/s]
+ 42%|     | 42/100 [00:03<00:04, 12.80it/s]
+ 44%|     | 44/100 [00:03<00:04, 13.02it/s]
+ 46%|     | 46/100 [00:03<00:04, 12.81it/s]
+ 48%|     | 48/100 [00:03<00:03, 13.04it/s]
+ 50%|     | 50/100 [00:03<00:03, 12.78it/s]
+ 52%|    | 52/100 [00:04<00:03, 12.61it/s]
+ 54%|    | 54/100 [00:04<00:03, 12.79it/s]
+ 56%|    | 56/100 [00:04<00:03, 12.85it/s]
+ 58%|    | 58/100 [00:04<00:03, 12.69it/s]
+ 60%|    | 60/100 [00:04<00:03, 12.65it/s]
+ 62%|   | 62/100 [00:04<00:02, 12.68it/s]
+ 64%|   | 64/100 [00:04<00:02, 12.82it/s]
+ 66%|   | 66/100 [00:05<00:02, 12.77it/s]
+ 68%|   | 68/100 [00:05<00:02, 12.78it/s]
+ 70%|   | 70/100 [00:05<00:02, 13.00it/s]
+ 72%|  | 72/100 [00:05<00:02, 13.18it/s]
+ 74%|  | 74/100 [00:05<00:01, 13.03it/s]
+ 76%|  | 76/100 [00:05<00:01, 13.24it/s]
+ 78%|  | 78/100 [00:06<00:01, 13.01it/s]
+ 80%|  | 80/100 [00:06<00:01, 13.11it/s]
+ 82%| | 82/100 [00:06<00:01, 13.11it/s]
+ 84%| | 84/100 [00:06<00:01, 12.93it/s]
+ 86%| | 86/100 [00:06<00:01, 12.63it/s]
+ 88%| | 88/100 [00:06<00:00, 12.66it/s]
+ 90%| | 90/100 [00:07<00:00, 12.68it/s]
+ 92%|| 92/100 [00:07<00:00, 12.47it/s]
+ 94%|| 94/100 [00:07<00:00, 12.66it/s]
+ 96%|| 96/100 [00:07<00:00, 12.89it/s]
+ 98%|| 98/100 [00:07<00:00, 12.76it/s]
+100%|| 100/100 [00:07<00:00, 12.48it/s]
+100%|| 100/100 [00:07<00:00, 12.80it/s]
+PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_sample_hyperparams PASSED
+tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_generate_with_conditional 
+  0%|          | 0/10000 [00:00<?, ?it/s]
+  0%|          | 1/10000 [00:00<25:22,  6.57it/s]
+  0%|          | 2/10000 [00:00<22:07,  7.53it/s]
+  0%|          | 3/10000 [00:00<20:36,  8.08it/s]
+  0%|          | 4/10000 [00:00<19:47,  8.41it/s]
+  0%|          | 5/10000 [00:00<19:58,  8.34it/s]
+  0%|          | 6/10000 [00:00<19:54,  8.36it/s]
+  0%|          | 7/10000 [00:00<19:28,  8.55it/s]
+  0%|          | 8/10000 [00:00<19:25,  8.57it/s]
+  0%|          | 9/10000 [00:01<19:33,  8.51it/s]
+  0%|          | 10/10000 [00:01<19:29,  8.54it/s]
+  0%|          | 11/10000 [00:01<19:25,  8.57it/s]
+  0%|          | 12/10000 [00:01<19:10,  8.68it/s]
+  0%|          | 13/10000 [00:01<19:25,  8.57it/s]
+  0%|          | 14/10000 [00:01<19:47,  8.41it/s]
+  0%|          | 15/10000 [00:01<19:37,  8.48it/s]
+  0%|          | 16/10000 [00:01<19:23,  8.58it/s]
+  0%|          | 17/10000 [00:02<19:14,  8.65it/s]
+  0%|          | 18/10000 [00:02<19:22,  8.59it/s]
+  0%|          | 19/10000 [00:02<19:20,  8.60it/s]
+  0%|          | 20/10000 [00:02<19:43,  8.43it/s]
+  0%|          | 21/10000 [00:02<19:28,  8.54it/s]
+  0%|          | 22/10000 [00:02<19:09,  8.68it/s]
+  0%|          | 23/10000 [00:02<18:55,  8.78it/s]
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+  0%|          | 28/10000 [00:03<18:33,  8.96it/s]
+  0%|          | 29/10000 [00:03<18:49,  8.83it/s]
+  0%|          | 30/10000 [00:03<18:36,  8.93it/s]
+  0%|          | 31/10000 [00:03<18:50,  8.82it/s]
+  0%|          | 32/10000 [00:03<18:48,  8.84it/s]
+  0%|          | 33/10000 [00:03<18:30,  8.97it/s]
+  0%|          | 34/10000 [00:03<18:37,  8.91it/s]
+  0%|          | 35/10000 [00:04<18:34,  8.94it/s]
+  0%|          | 36/10000 [00:04<19:16,  8.62it/s]
+  0%|          | 37/10000 [00:04<19:02,  8.72it/s]
+  0%|          | 38/10000 [00:04<19:15,  8.62it/s]
+  0%|          | 39/10000 [00:04<19:17,  8.60it/s]
+  0%|          | 40/10000 [00:04<19:32,  8.49it/s]
+  0%|          | 41/10000 [00:04<19:17,  8.60it/s]
+  0%|          | 42/10000 [00:04<19:27,  8.53it/s]
+  0%|          | 43/10000 [00:04<19:11,  8.65it/s]
+  0%|          | 44/10000 [00:05<19:07,  8.68it/s]
+  0%|          | 45/10000 [00:05<19:06,  8.68it/s]
+  0%|          | 46/10000 [00:05<18:50,  8.81it/s]
+  0%|          | 47/10000 [00:05<18:44,  8.85it/s]
+  0%|          | 48/10000 [00:05<18:30,  8.96it/s]
+  0%|          | 49/10000 [00:05<18:25,  9.00it/s]
+  0%|          | 50/10000 [00:06<1:14:26,  2.23it/s]
+  1%|          | 51/10000 [00:07<59:25,  2.79it/s]  
+  1%|          | 52/10000 [00:07<46:59,  3.53it/s]
+  1%|          | 53/10000 [00:07<38:58,  4.25it/s]
+  1%|          | 54/10000 [00:07<32:58,  5.03it/s]
+  1%|          | 55/10000 [00:07<28:49,  5.75it/s]
+  1%|          | 56/10000 [00:07<26:00,  6.37it/s]
+  1%|          | 57/10000 [00:07<24:23,  6.79it/s]
+  1%|          | 58/10000 [00:07<22:55,  7.23it/s]
+  1%|          | 59/10000 [00:07<21:32,  7.69it/s]
+  1%|          | 60/10000 [00:08<20:46,  7.97it/s]
+  1%|          | 61/10000 [00:08<20:20,  8.15it/s]
+  1%|          | 62/10000 [00:08<19:49,  8.35it/s]
+  1%|          | 63/10000 [00:08<19:45,  8.38it/s]
+  1%|          | 64/10000 [00:08<19:32,  8.47it/s]
+  1%|          | 65/10000 [00:08<19:14,  8.61it/s]
+  1%|          | 66/10000 [00:08<19:20,  8.56it/s]
+  1%|          | 67/10000 [00:08<19:10,  8.63it/s]
+  1%|          | 68/10000 [00:09<19:06,  8.66it/s]
+  1%|          | 69/10000 [00:09<19:27,  8.51it/s]
+  1%|          | 70/10000 [00:09<19:14,  8.60it/s]
+  1%|          | 71/10000 [00:09<19:11,  8.62it/s]
+  1%|          | 72/10000 [00:09<19:29,  8.49it/s]
+  1%|          | 73/10000 [00:09<19:18,  8.57it/s]
+  1%|          | 74/10000 [00:09<19:18,  8.57it/s]
+  1%|          | 75/10000 [00:09<19:04,  8.67it/s]
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+  1%|          | 78/10000 [00:10<19:04,  8.67it/s]
+  1%|          | 79/10000 [00:10<19:12,  8.61it/s]
+  1%|          | 80/10000 [00:10<19:39,  8.41it/s]
+  1%|          | 81/10000 [00:10<19:37,  8.42it/s]
+  1%|          | 82/10000 [00:10<19:28,  8.49it/s]
+  1%|          | 83/10000 [00:10<19:18,  8.56it/s]
+  1%|          | 84/10000 [00:10<19:25,  8.51it/s]
+  1%|          | 85/10000 [00:10<19:20,  8.54it/s]
+  1%|          | 86/10000 [00:11<19:27,  8.49it/s]
+  1%|          | 87/10000 [00:11<19:26,  8.50it/s]
+  1%|          | 88/10000 [00:11<19:22,  8.53it/s]
+  1%|          | 89/10000 [00:11<19:40,  8.40it/s]
+  1%|          | 90/10000 [00:11<19:45,  8.36it/s]
+  1%|          | 91/10000 [00:11<19:31,  8.46it/s]
+  1%|          | 92/10000 [00:11<19:25,  8.50it/s]
+  1%|          | 93/10000 [00:11<19:47,  8.34it/s]
+  1%|          | 94/10000 [00:12<19:43,  8.37it/s]
+  1%|          | 95/10000 [00:12<19:48,  8.33it/s]
+  1%|          | 96/10000 [00:12<19:52,  8.31it/s]
+  1%|          | 97/10000 [00:12<19:38,  8.41it/s]
+  1%|          | 98/10000 [00:12<19:20,  8.53it/s]
+  1%|          | 99/10000 [00:12<19:44,  8.36it/s]
+  1%|          | 100/10000 [00:13<1:19:28,  2.08it/s]
+  1%|          | 101/10000 [00:14<1:02:48,  2.63it/s]
+  1%|          | 102/10000 [00:14<49:39,  3.32it/s]  
+  1%|          | 103/10000 [00:14<40:29,  4.07it/s]
+  1%|          | 104/10000 [00:14<33:57,  4.86it/s]
+  1%|          | 105/10000 [00:14<29:41,  5.55it/s]
+  1%|          | 106/10000 [00:14<26:43,  6.17it/s]
+  1%|          | 107/10000 [00:14<24:38,  6.69it/s]
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+PASSED
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+tests/plugins/time_series/test_fflows.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_name[test_plugin0] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_name[test_plugin1] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_name[test_plugin2] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_type[test_plugin0] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_type[test_plugin1] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_type[test_plugin2] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/time_series/test_fflows.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/time_series/test_fflows.py::test_sample_hyperparams PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_sanity[test_plugin0] PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_sanity[test_plugin1] PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_sanity[test_plugin2] PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_name[test_plugin0] PASSED
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+tests/plugins/time_series/test_timegan.py::test_plugin_hyperparams[test_plugin0] PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_hyperparams[test_plugin1] PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_hyperparams[test_plugin2] PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_fit 
+  0%|          | 0/10 [00:00<?, ?it/s]
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+100%|| 10/10 [00:05<00:00,  1.78it/s]
+100%|| 10/10 [00:05<00:00,  1.78it/s]
+PASSED
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+100%|| 10/10 [00:10<00:00,  1.04s/it]
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+PASSED
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+PASSED
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+100%|| 10/10 [00:10<00:00,  1.04s/it]
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+FAILED
+tests/plugins/time_series/test_timegan.py::test_plugin_generate_static_cond[source1] 
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+FAILED
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+PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_generate_horizons_cond[source1] 
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+ 90%| | 9/10 [00:05<00:00,  1.77it/s]
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+100%|| 10/10 [00:05<00:00,  1.76it/s]
+PASSED
+tests/plugins/time_series/test_timegan.py::test_sample_hyperparams PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_generate_survival_sampler[none] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:05<00:48,  5.37s/it]
+ 20%|        | 2/10 [00:10<00:42,  5.33s/it]
+ 30%|       | 3/10 [00:16<00:37,  5.36s/it]
+ 40%|      | 4/10 [00:21<00:32,  5.34s/it]
+ 50%|     | 5/10 [00:26<00:26,  5.33s/it]
+ 60%|    | 6/10 [00:32<00:21,  5.34s/it]
+ 70%|   | 7/10 [00:37<00:16,  5.50s/it]
+ 80%|  | 8/10 [00:43<00:11,  5.64s/it]
+ 90%| | 9/10 [00:49<00:05,  5.70s/it]
+100%|| 10/10 [00:55<00:00,  5.67s/it]
+100%|| 10/10 [00:55<00:00,  5.53s/it]
+PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_generate_survival_sampler[imbalanced_censoring] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:04<00:38,  4.30s/it]
+ 20%|        | 2/10 [00:08<00:34,  4.27s/it]
+ 30%|       | 3/10 [00:12<00:29,  4.28s/it]
+ 40%|      | 4/10 [00:17<00:25,  4.28s/it]
+ 50%|     | 5/10 [00:21<00:21,  4.27s/it]
+ 60%|    | 6/10 [00:25<00:17,  4.29s/it]
+ 70%|   | 7/10 [00:29<00:12,  4.28s/it]
+ 80%|  | 8/10 [00:34<00:08,  4.31s/it]
+ 90%| | 9/10 [00:38<00:04,  4.40s/it]
+100%|| 10/10 [00:43<00:00,  4.50s/it]
+100%|| 10/10 [00:43<00:00,  4.36s/it]
+PASSED
+tests/plugins/time_series/test_timegan.py::test_plugin_generate_survival_sampler[imbalanced_time_censoring] 
+  0%|          | 0/10 [00:00<?, ?it/s]
+ 10%|         | 1/10 [00:04<00:38,  4.33s/it]
+ 20%|        | 2/10 [00:08<00:34,  4.29s/it]
+ 30%|       | 3/10 [00:12<00:29,  4.27s/it]
+ 40%|      | 4/10 [00:17<00:25,  4.27s/it]
+ 50%|     | 5/10 [00:21<00:21,  4.27s/it]
+ 60%|    | 6/10 [00:25<00:17,  4.27s/it]
+ 70%|   | 7/10 [00:29<00:12,  4.28s/it]
+ 80%|  | 8/10 [00:34<00:08,  4.30s/it]
+ 90%| | 9/10 [00:38<00:04,  4.37s/it]
+100%|| 10/10 [00:43<00:00,  4.41s/it]
+100%|| 10/10 [00:43<00:00,  4.33s/it]
+PASSED
+tests/plugins/time_series/test_ts_marginal_distributions.py::test_plugin_fit[test_plugin0] PASSED
+tests/plugins/time_series/test_ts_marginal_distributions.py::test_plugin_fit[test_plugin1] PASSED
+tests/plugins/time_series/test_ts_marginal_distributions.py::test_plugin_fit[test_plugin2] PASSED
+tests/plugins/time_series/test_ts_marginal_distributions.py::test_plugin_generate[source0-test_plugin0] PASSED
+tests/plugins/time_series/test_ts_marginal_distributions.py::test_plugin_generate[source1-test_plugin0] PASSED
+tests/plugins/time_series/test_ts_marginal_distributions.py::test_plugin_generate[source2-test_plugin0] PASSED
+tests/plugins/time_series/test_ts_marginal_distributions.py::test_plugin_generate_survival PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_sanity PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_get_spans PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_split PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_partition PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_validation PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_get_categoricals PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_get_freqs PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_test_partition PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_anonymize_column PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_anonymize_columns PASSED
+tests/utils/test_anonymization.py::test_k_anonymity_anonymize_full PASSED
+tests/utils/test_anonymization.py::test_l_diversity_sanity PASSED
+tests/utils/test_anonymization.py::test_l_diversity_get_spans PASSED
+tests/utils/test_anonymization.py::test_l_diversity_split PASSED
+tests/utils/test_anonymization.py::test_l_diversity_partition PASSED
+tests/utils/test_anonymization.py::test_l_diversity_validation PASSED
+tests/utils/test_anonymization.py::test_l_diversity_anonymize_column PASSED
+tests/utils/test_anonymization.py::test_l_diversity_anonymize_columns PASSED
+tests/utils/test_anonymization.py::test_l_diversity_anonymize_full PASSED
+tests/utils/test_anonymization.py::test_t_closeness_sanity PASSED
+tests/utils/test_anonymization.py::test_t_closeness_get_spans PASSED
+tests/utils/test_anonymization.py::test_t_closeness_split PASSED
+tests/utils/test_anonymization.py::test_t_closeness_partition PASSED
+tests/utils/test_anonymization.py::test_t_closeness_validation PASSED
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_column[0.2] PASSED
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_column[0.5] PASSED
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_columns[0.2] PASSED
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_columns[0.5] PASSED
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.2] PASSED
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.5] PASSED
+tests/utils/test_compression.py::test_compression_sanity PASSED
+tests/utils/test_compression.py::test_decompression_sanity PASSED
+tests/utils/test_compression.py::test_compression_sanity2 PASSED
+tests/utils/test_compression.py::test_compression_sanity_airfoil PASSED
+tests/utils/test_compression.py::test_decompression_sanity_airfoil PASSED
+tests/utils/test_serialization.py::test_save_load PASSED
+tests/utils/datasets/time_series/test_google_stocks.py::test_dataloader PASSED
+tests/utils/datasets/time_series/test_pbc.py::test_dataloader[True] PASSED
+tests/utils/datasets/time_series/test_pbc.py::test_dataloader[False] PASSED
+tests/utils/datasets/time_series/test_sine.py::test_dataloader PASSED
+
+====================================================== FAILURES ======================================================
+________________ test_evaluate_performance_time_series_survival[PerformanceEvaluatorXGB-test_plugin0] ________________
+
+test_plugin = <synthcity.plugins.generic.plugin_marginal_distributions.MarginalDistributionPlugin object at 0x14ff8ecee210>
+evaluator_t = <class 'synthcity.metrics.eval_performance.PerformanceEvaluatorXGB'>
+
+    @pytest.mark.parametrize("test_plugin", [Plugins().get("marginal_distributions")])
+    @pytest.mark.parametrize(
+        "evaluator_t",
+        [
+            PerformanceEvaluatorLinear,
+            PerformanceEvaluatorMLP,
+            PerformanceEvaluatorXGB,
+        ],
+    )
+    @pytest.mark.skipif(sys.platform != "linux", reason="Linux only for faster results")
+    def test_evaluate_performance_time_series_survival(
+        test_plugin: Plugin, evaluator_t: Type
+    ) -> None:
+        static_data, temporal_data, observation_times, outcome = PBCDataloader().load()
+    
+        T, E = outcome
+    
+        data = TimeSeriesSurvivalDataLoader(
+            temporal_data=temporal_data,
+            observation_times=observation_times,
+            static_data=static_data,
+            T=T,
+            E=E,
+        )
+    
+        test_plugin.fit(data)
+        data_gen = test_plugin.generate(len(temporal_data))
+    
+        evaluator = evaluator_t(
+            task_type="time_series_survival",
+        )
+    
+>       good_score = evaluator.evaluate(
+            data,
+            data_gen,
+        )
+
+tests/metrics/test_performance.py:461: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_performance.py:700: in evaluate
+    return self._evaluate_time_series_survival_performance(
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_performance.py:534: in _evaluate_time_series_survival_performance
+    score_gt = evaluate_ts_survival_model(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/benchmarks.py:309: in evaluate_ts_survival_model
+    c_index, brier_score = _get_surv_metrics(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/benchmarks.py:221: in _get_surv_metrics
+    model.fit(
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_xgb.py:108: in fit
+    self.pred_model.fit(pd.DataFrame(embeddings), pd.Series(T), pd.Series(E))
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_xgb.py:112: in fit
+    self.model.fit(X, y, time_bins=time_bins)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/synthcity/0.2.10-foss-2023a/lib/python3.11/site-packages/xgbse/_debiased_bce.py:232: in fit
+    dtrain, pred_leaf=True, iteration_range=(0, self.bst.best_iteration + 1)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+self = <xgboost.core.Booster object at 0x14ff63b94a10>
+
+    @property
+    def best_iteration(self) -> int:
+        """The best iteration during training."""
+        best = self.attr("best_iteration")
+        if best is not None:
+            return int(best)
+    
+>       raise AttributeError(
+            "`best_iteration` is only defined when early stopping is used."
+        )
+E       AttributeError: `best_iteration` is only defined when early stopping is used.
+
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/XGBoost/2.0.2-gfbf-2023a/lib/python3.11/site-packages/xgboost/core.py:2603: AttributeError
+__________________________________________________ test_schema_fail __________________________________________________
+
+    def test_schema_fail() -> None:
+        if pydantic.__version__ < "2":
+            with pytest.raises(pydantic.error_wrappers.ValidationError):
+                Schema(data="sdfsfs")
+        else:
+>           with pytest.raises(pydantic.pydantic_core._pydantic_core.ValidationError):
+
+tests/plugins/core/test_schema.py:17: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/__init__.py:363: in __getattr__
+    return _getattr_migration(attr_name)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+name = 'pydantic_core'
+
+    def wrapper(name: str) -> object:
+        """Raise an error if the object is not found, or warn if it was moved.
+    
+        In case it was moved, it still returns the object.
+    
+        Args:
+            name: The object name.
+    
+        Returns:
+            The object.
+        """
+        if name == '__path__':
+            raise AttributeError(f'module {module!r} has no attribute {name!r}')
+    
+        import warnings
+    
+        from ._internal._validators import import_string
+    
+        import_path = f'{module}:{name}'
+        if import_path in MOVED_IN_V2.keys():
+            new_location = MOVED_IN_V2[import_path]
+            warnings.warn(f'`{import_path}` has been moved to `{new_location}`.')
+            return import_string(MOVED_IN_V2[import_path])
+        if import_path in DEPRECATED_MOVED_IN_V2:
+            # skip the warning here because a deprecation warning will be raised elsewhere
+            return import_string(DEPRECATED_MOVED_IN_V2[import_path])
+        if import_path in REDIRECT_TO_V1:
+            new_location = REDIRECT_TO_V1[import_path]
+            warnings.warn(
+                f'`{import_path}` has been removed. We are importing from `{new_location}` instead.'
+                'See the migration guide for more details: https://docs.pydantic.dev/latest/migration/'
+            )
+            return import_string(REDIRECT_TO_V1[import_path])
+        if import_path == 'pydantic:BaseSettings':
+            raise PydanticImportError(
+                '`BaseSettings` has been moved to the `pydantic-settings` package. '
+                f'See https://docs.pydantic.dev/{version_short()}/migration/#basesettings-has-moved-to-pydantic-settings '
+                'for more details.'
+            )
+        if import_path in REMOVED_IN_V2:
+            raise PydanticImportError(f'`{import_path}` has been removed in V2.')
+        globals: Dict[str, Any] = sys.modules[module].__dict__
+        if name in globals:
+            return globals[name]
+>       raise AttributeError(f'module {module!r} has no attribute {name!r}')
+E       AttributeError: module 'pydantic' has no attribute 'pydantic_core'
+
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/_migration.py:306: AttributeError
+_______________________________________________ test_train_prediction ________________________________________________
+
+    def test_train_prediction() -> None:
+        df = load_rossi()
+    
+        X = df.drop(["week", "arrest"], axis=1)
+        Y = df["arrest"]
+        T = df["week"]
+        time_horizons = np.linspace(T.min(), T.max(), num=4)[1:-1].tolist()
+    
+        model = XGBSurvivalAnalysis(n_estimators=10)
+    
+>       model.fit(X, T, Y)
+
+tests/plugins/core/models/survival_analysis/test_surv_xgb.py:33: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_xgb.py:112: in fit
+    self.model.fit(X, y, time_bins=time_bins)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/synthcity/0.2.10-foss-2023a/lib/python3.11/site-packages/xgbse/_debiased_bce.py:232: in fit
+    dtrain, pred_leaf=True, iteration_range=(0, self.bst.best_iteration + 1)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+self = <xgboost.core.Booster object at 0x14fefa881b90>
+
+    @property
+    def best_iteration(self) -> int:
+        """The best iteration during training."""
+        best = self.attr("best_iteration")
+        if best is not None:
+            return int(best)
+    
+>       raise AttributeError(
+            "`best_iteration` is only defined when early stopping is used."
+        )
+E       AttributeError: `best_iteration` is only defined when early stopping is used.
+
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/XGBoost/2.0.2-gfbf-2023a/lib/python3.11/site-packages/xgboost/core.py:2603: AttributeError
+_________________________________________ test_train_prediction[Transformer] _________________________________________
+
+emb_rnn_type = 'Transformer'
+
+    @pytest.mark.parametrize("emb_rnn_type", ["Transformer"])
+    def test_train_prediction(emb_rnn_type: str) -> None:
+        static, temporal, observation_times, outcome = PBCDataloader(as_numpy=True).load()
+        T, E = outcome
+    
+        horizons = [0.25, 0.5, 0.75]
+        time_horizons = np.quantile(T, horizons).tolist()
+    
+        model = XGBTimeSeriesSurvival(emb_rnn_type=emb_rnn_type)
+>       score = evaluate_ts_survival_model(
+            model, static, temporal, observation_times, T, E, time_horizons
+        )
+
+tests/plugins/core/models/time_series_survival/test_ts_surv_xgb.py:39: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/benchmarks.py:309: in evaluate_ts_survival_model
+    c_index, brier_score = _get_surv_metrics(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/benchmarks.py:221: in _get_surv_metrics
+    model.fit(
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_xgb.py:108: in fit
+    self.pred_model.fit(pd.DataFrame(embeddings), pd.Series(T), pd.Series(E))
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_xgb.py:112: in fit
+    self.model.fit(X, y, time_bins=time_bins)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/synthcity/0.2.10-foss-2023a/lib/python3.11/site-packages/xgbse/_kaplan_neighbors.py:179: in fit
+    dtrain, pred_leaf=True, iteration_range=(0, self.bst.best_iteration + 1)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+self = <xgboost.core.Booster object at 0x14fef78d4a10>
+
+    @property
+    def best_iteration(self) -> int:
+        """The best iteration during training."""
+        best = self.attr("best_iteration")
+        if best is not None:
+            return int(best)
+    
+>       raise AttributeError(
+            "`best_iteration` is only defined when early stopping is used."
+        )
+E       AttributeError: `best_iteration` is only defined when early stopping is used.
+
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/XGBoost/2.0.2-gfbf-2023a/lib/python3.11/site-packages/xgboost/core.py:2603: AttributeError
+_______________________________________________ test_train_prediction ________________________________________________
+
+    def test_train_prediction() -> None:
+        df = load_rossi()
+    
+        X = df.drop(["week", "arrest"], axis=1)
+        Y = df["arrest"]
+        T = df["week"]
+    
+        model = XGBTimeToEvent(n_estimators=10)
+    
+>       model.fit(X, T, Y)
+
+tests/plugins/core/models/time_to_event/test_tte_xgb.py:31: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_xgb.py:111: in fit
+    self.model.fit(X, y, time_bins=time_bins)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/synthcity/0.2.10-foss-2023a/lib/python3.11/site-packages/xgbse/_debiased_bce.py:232: in fit
+    dtrain, pred_leaf=True, iteration_range=(0, self.bst.best_iteration + 1)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+self = <xgboost.core.Booster object at 0x14fef7874d10>
+
+    @property
+    def best_iteration(self) -> int:
+        """The best iteration during training."""
+        best = self.attr("best_iteration")
+        if best is not None:
+            return int(best)
+    
+>       raise AttributeError(
+            "`best_iteration` is only defined when early stopping is used."
+        )
+E       AttributeError: `best_iteration` is only defined when early stopping is used.
+
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/XGBoost/2.0.2-gfbf-2023a/lib/python3.11/site-packages/xgboost/core.py:2603: AttributeError
+_________________________________________ test_plugin_generate[test_plugin0] _________________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_decaf.DECAFPlugin object at 0x14ff8b67d7d0>
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    @pytest.mark.skipif(sys.platform != "linux", reason="Linux only for faster results")
+    def test_plugin_generate(test_plugin: Plugin) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_decaf.py:110: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_decaf.py:437: in _fit
+    trainer.fit(self.model, dm)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:544: in fit
+    call._call_and_handle_interrupt(
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/call.py:44: in _call_and_handle_interrupt
+    return trainer_fn(*args, **kwargs)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:580: in _fit_impl
+    self._run(model, ckpt_path=ckpt_path)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:963: in _run
+    self.strategy.setup(self)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/strategies/strategy.py:159: in setup
+    self.setup_optimizers(trainer)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/strategies/strategy.py:139: in setup_optimizers
+    self.optimizers, self.lr_scheduler_configs = _init_optimizers_and_lr_schedulers(self.lightning_module)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/core/optimizer.py:192: in _init_optimizers_and_lr_schedulers
+    _validate_multiple_optimizers_support(optimizers, model)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+optimizers = [AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+...ble: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.01
+)]
+model = DECAF(
+  (generator): Generator_causal(
+    (shared): Sequential(
+      (0): Linear(in_features=500, out_features=500,...ut_features=500, bias=True)
+      (3): ReLU()
+      (4): Linear(in_features=500, out_features=1, bias=True)
+    )
+  )
+)
+
+    def _validate_multiple_optimizers_support(optimizers: List[Optimizer], model: "pl.LightningModule") -> None:
+        if is_param_in_hook_signature(model.training_step, "optimizer_idx", explicit=True):
+>           raise RuntimeError(
+                "Training with multiple optimizers is only supported with manual optimization. Remove the `optimizer_idx`"
+                " argument from `training_step`, set `self.automatic_optimization = False` and access your optimizers"
+                " in `training_step` with `opt1, opt2, ... = self.optimizers()`."
+            )
+E           RuntimeError: Training with multiple optimizers is only supported with manual optimization. Remove the `optimizer_idx` argument from `training_step`, set `self.automatic_optimization = False` and access your optimizers in `training_step` with `opt1, opt2, ... = self.optimizers()`.
+
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/core/optimizer.py:351: RuntimeError
+------------------------------------------------- Captured log call --------------------------------------------------
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 GPU available: False, used: False
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 TPU available: False, using: 0 TPU cores
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 IPU available: False, using: 0 IPUs
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 HPU available: False, using: 0 HPUs
+_________________________________________ test_plugin_generate[test_plugin1] _________________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_decaf.DECAFPlugin object at 0x14ff8ba54210>
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    @pytest.mark.skipif(sys.platform != "linux", reason="Linux only for faster results")
+    def test_plugin_generate(test_plugin: Plugin) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_decaf.py:110: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_decaf.py:437: in _fit
+    trainer.fit(self.model, dm)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:544: in fit
+    call._call_and_handle_interrupt(
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/call.py:44: in _call_and_handle_interrupt
+    return trainer_fn(*args, **kwargs)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:580: in _fit_impl
+    self._run(model, ckpt_path=ckpt_path)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:963: in _run
+    self.strategy.setup(self)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/strategies/strategy.py:159: in setup
+    self.setup_optimizers(trainer)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/strategies/strategy.py:139: in setup_optimizers
+    self.optimizers, self.lr_scheduler_configs = _init_optimizers_and_lr_schedulers(self.lightning_module)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/core/optimizer.py:192: in _init_optimizers_and_lr_schedulers
+    _validate_multiple_optimizers_support(optimizers, model)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+optimizers = [AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+...ble: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.01
+)]
+model = DECAF(
+  (generator): Generator_causal(
+    (shared): Sequential(
+      (0): Linear(in_features=500, out_features=500,...ut_features=500, bias=True)
+      (3): ReLU()
+      (4): Linear(in_features=500, out_features=1, bias=True)
+    )
+  )
+)
+
+    def _validate_multiple_optimizers_support(optimizers: List[Optimizer], model: "pl.LightningModule") -> None:
+        if is_param_in_hook_signature(model.training_step, "optimizer_idx", explicit=True):
+>           raise RuntimeError(
+                "Training with multiple optimizers is only supported with manual optimization. Remove the `optimizer_idx`"
+                " argument from `training_step`, set `self.automatic_optimization = False` and access your optimizers"
+                " in `training_step` with `opt1, opt2, ... = self.optimizers()`."
+            )
+E           RuntimeError: Training with multiple optimizers is only supported with manual optimization. Remove the `optimizer_idx` argument from `training_step`, set `self.automatic_optimization = False` and access your optimizers in `training_step` with `opt1, opt2, ... = self.optimizers()`.
+
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/core/optimizer.py:351: RuntimeError
+------------------------------------------------- Captured log call --------------------------------------------------
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 GPU available: False, used: False
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 TPU available: False, using: 0 TPU cores
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 IPU available: False, using: 0 IPUs
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 HPU available: False, using: 0 HPUs
+_________________________________________ test_plugin_generate[test_plugin2] _________________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_decaf.DECAFPlugin object at 0x14ff8b67df90>
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    @pytest.mark.skipif(sys.platform != "linux", reason="Linux only for faster results")
+    def test_plugin_generate(test_plugin: Plugin) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_decaf.py:110: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_decaf.py:437: in _fit
+    trainer.fit(self.model, dm)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:544: in fit
+    call._call_and_handle_interrupt(
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/call.py:44: in _call_and_handle_interrupt
+    return trainer_fn(*args, **kwargs)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:580: in _fit_impl
+    self._run(model, ckpt_path=ckpt_path)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/trainer.py:963: in _run
+    self.strategy.setup(self)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/strategies/strategy.py:159: in setup
+    self.setup_optimizers(trainer)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/strategies/strategy.py:139: in setup_optimizers
+    self.optimizers, self.lr_scheduler_configs = _init_optimizers_and_lr_schedulers(self.lightning_module)
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/core/optimizer.py:192: in _init_optimizers_and_lr_schedulers
+    _validate_multiple_optimizers_support(optimizers, model)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+optimizers = [AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+...ble: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.01
+)]
+model = DECAF(
+  (generator): Generator_causal(
+    (shared): Sequential(
+      (0): Linear(in_features=500, out_features=500,...ut_features=500, bias=True)
+      (3): ReLU()
+      (4): Linear(in_features=500, out_features=1, bias=True)
+    )
+  )
+)
+
+    def _validate_multiple_optimizers_support(optimizers: List[Optimizer], model: "pl.LightningModule") -> None:
+        if is_param_in_hook_signature(model.training_step, "optimizer_idx", explicit=True):
+>           raise RuntimeError(
+                "Training with multiple optimizers is only supported with manual optimization. Remove the `optimizer_idx`"
+                " argument from `training_step`, set `self.automatic_optimization = False` and access your optimizers"
+                " in `training_step` with `opt1, opt2, ... = self.optimizers()`."
+            )
+E           RuntimeError: Training with multiple optimizers is only supported with manual optimization. Remove the `optimizer_idx` argument from `training_step`, set `self.automatic_optimization = False` and access your optimizers in `training_step` with `opt1, opt2, ... = self.optimizers()`.
+
+/scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/core/optimizer.py:351: RuntimeError
+------------------------------------------------- Captured log call --------------------------------------------------
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 GPU available: False, used: False
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 TPU available: False, using: 0 TPU cores
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 IPU available: False, using: 0 IPUs
+INFO     pytorch_lightning.utilities.rank_zero:rank_zero.py:64 HPU available: False, using: 0 HPUs
+______________________________________ test_plugin_generate[True-test_plugin1] _______________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_dpgan.DPGANPlugin object at 0x14ff8b6fded0>, serialize = True
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    @pytest.mark.parametrize("serialize", [True, False])
+    def test_plugin_generate(test_plugin: Plugin, serialize: bool) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_dpgan.py:61: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py:287: in _fit
+    self.model.fit(X.dataframe(), cond=cond)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py:358: in fit
+    self.model.fit(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:278: in fit
+    self._train(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:616: in _train
+    g_loss, d_loss = self._train_epoch(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:493: in _train_epoch
+    self._train_epoch_discriminator(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:462: in _train_epoch_discriminator
+    self.discriminator.optimizer.step()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+args = (Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+),)
+kwargs = {}
+self = Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+)
+_ = [], profile_name = 'Optimizer.step#DPOptimizer.step'
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+        self, *_ = args
+        self = cast(Optimizer, self)
+        profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+        with torch.autograd.profiler.record_function(profile_name):
+            # call optimizer step pre hooks
+>           for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
+E           AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+
+/apps/gent/RHEL8/cascadelake-ib/software/PyTorch/2.1.2-foss-2023a/lib/python3.11/site-packages/torch/optim/optimizer.py:363: AttributeError
+______________________________________ test_plugin_generate[True-test_plugin2] _______________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_dpgan.DPGANPlugin object at 0x14ff8b8e6590>, serialize = True
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    @pytest.mark.parametrize("serialize", [True, False])
+    def test_plugin_generate(test_plugin: Plugin, serialize: bool) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_dpgan.py:61: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py:287: in _fit
+    self.model.fit(X.dataframe(), cond=cond)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py:358: in fit
+    self.model.fit(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:278: in fit
+    self._train(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:616: in _train
+    g_loss, d_loss = self._train_epoch(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:493: in _train_epoch
+    self._train_epoch_discriminator(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:462: in _train_epoch_discriminator
+    self.discriminator.optimizer.step()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+args = (Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+),)
+kwargs = {}
+self = Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+)
+_ = [], profile_name = 'Optimizer.step#DPOptimizer.step'
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+        self, *_ = args
+        self = cast(Optimizer, self)
+        profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+        with torch.autograd.profiler.record_function(profile_name):
+            # call optimizer step pre hooks
+>           for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
+E           AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+
+/apps/gent/RHEL8/cascadelake-ib/software/PyTorch/2.1.2-foss-2023a/lib/python3.11/site-packages/torch/optim/optimizer.py:363: AttributeError
+______________________________________ test_plugin_generate[False-test_plugin0] ______________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_dpgan.DPGANPlugin object at 0x14ff8b71ae50>, serialize = False
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    @pytest.mark.parametrize("serialize", [True, False])
+    def test_plugin_generate(test_plugin: Plugin, serialize: bool) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_dpgan.py:61: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py:287: in _fit
+    self.model.fit(X.dataframe(), cond=cond)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py:358: in fit
+    self.model.fit(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:278: in fit
+    self._train(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:616: in _train
+    g_loss, d_loss = self._train_epoch(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:493: in _train_epoch
+    self._train_epoch_discriminator(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:462: in _train_epoch_discriminator
+    self.discriminator.optimizer.step()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+args = (Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+),)
+kwargs = {}
+self = Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+)
+_ = [], profile_name = 'Optimizer.step#DPOptimizer.step'
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+        self, *_ = args
+        self = cast(Optimizer, self)
+        profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+        with torch.autograd.profiler.record_function(profile_name):
+            # call optimizer step pre hooks
+>           for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
+E           AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+
+/apps/gent/RHEL8/cascadelake-ib/software/PyTorch/2.1.2-foss-2023a/lib/python3.11/site-packages/torch/optim/optimizer.py:363: AttributeError
+______________________________________ test_plugin_generate[False-test_plugin1] ______________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_dpgan.DPGANPlugin object at 0x14ff8b6fded0>, serialize = False
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    @pytest.mark.parametrize("serialize", [True, False])
+    def test_plugin_generate(test_plugin: Plugin, serialize: bool) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_dpgan.py:61: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py:287: in _fit
+    self.model.fit(X.dataframe(), cond=cond)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py:358: in fit
+    self.model.fit(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:278: in fit
+    self._train(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:616: in _train
+    g_loss, d_loss = self._train_epoch(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:493: in _train_epoch
+    self._train_epoch_discriminator(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:462: in _train_epoch_discriminator
+    self.discriminator.optimizer.step()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+args = (Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+),)
+kwargs = {}
+self = Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+)
+_ = [], profile_name = 'Optimizer.step#DPOptimizer.step'
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+        self, *_ = args
+        self = cast(Optimizer, self)
+        profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+        with torch.autograd.profiler.record_function(profile_name):
+            # call optimizer step pre hooks
+>           for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
+E           AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+
+/apps/gent/RHEL8/cascadelake-ib/software/PyTorch/2.1.2-foss-2023a/lib/python3.11/site-packages/torch/optim/optimizer.py:363: AttributeError
+______________________________________ test_plugin_generate[False-test_plugin2] ______________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_dpgan.DPGANPlugin object at 0x14ff8b8e6590>, serialize = False
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    @pytest.mark.parametrize("serialize", [True, False])
+    def test_plugin_generate(test_plugin: Plugin, serialize: bool) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_dpgan.py:61: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py:287: in _fit
+    self.model.fit(X.dataframe(), cond=cond)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py:358: in fit
+    self.model.fit(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:278: in fit
+    self._train(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:616: in _train
+    g_loss, d_loss = self._train_epoch(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:493: in _train_epoch
+    self._train_epoch_discriminator(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:462: in _train_epoch_discriminator
+    self.discriminator.optimizer.step()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+args = (Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+),)
+kwargs = {}
+self = Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+)
+_ = [], profile_name = 'Optimizer.step#DPOptimizer.step'
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+        self, *_ = args
+        self = cast(Optimizer, self)
+        profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+        with torch.autograd.profiler.record_function(profile_name):
+            # call optimizer step pre hooks
+>           for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
+E           AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+
+/apps/gent/RHEL8/cascadelake-ib/software/PyTorch/2.1.2-foss-2023a/lib/python3.11/site-packages/torch/optim/optimizer.py:363: AttributeError
+___________________________________ test_plugin_generate_constraints[test_plugin0] ___________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_dpgan.DPGANPlugin object at 0x14ff8b6a5890>
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    def test_plugin_generate_constraints(test_plugin: Plugin) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_dpgan.py:81: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py:287: in _fit
+    self.model.fit(X.dataframe(), cond=cond)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py:358: in fit
+    self.model.fit(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:278: in fit
+    self._train(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:616: in _train
+    g_loss, d_loss = self._train_epoch(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:493: in _train_epoch
+    self._train_epoch_discriminator(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:462: in _train_epoch_discriminator
+    self.discriminator.optimizer.step()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+args = (Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+),)
+kwargs = {}
+self = Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+)
+_ = [], profile_name = 'Optimizer.step#DPOptimizer.step'
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+        self, *_ = args
+        self = cast(Optimizer, self)
+        profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+        with torch.autograd.profiler.record_function(profile_name):
+            # call optimizer step pre hooks
+>           for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
+E           AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+
+/apps/gent/RHEL8/cascadelake-ib/software/PyTorch/2.1.2-foss-2023a/lib/python3.11/site-packages/torch/optim/optimizer.py:363: AttributeError
+___________________________________ test_plugin_generate_constraints[test_plugin1] ___________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_dpgan.DPGANPlugin object at 0x14ff8b8e7fd0>
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    def test_plugin_generate_constraints(test_plugin: Plugin) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_dpgan.py:81: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py:287: in _fit
+    self.model.fit(X.dataframe(), cond=cond)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py:358: in fit
+    self.model.fit(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:278: in fit
+    self._train(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:616: in _train
+    g_loss, d_loss = self._train_epoch(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:493: in _train_epoch
+    self._train_epoch_discriminator(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:462: in _train_epoch_discriminator
+    self.discriminator.optimizer.step()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+args = (Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+),)
+kwargs = {}
+self = Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+)
+_ = [], profile_name = 'Optimizer.step#DPOptimizer.step'
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+        self, *_ = args
+        self = cast(Optimizer, self)
+        profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+        with torch.autograd.profiler.record_function(profile_name):
+            # call optimizer step pre hooks
+>           for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
+E           AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+
+/apps/gent/RHEL8/cascadelake-ib/software/PyTorch/2.1.2-foss-2023a/lib/python3.11/site-packages/torch/optim/optimizer.py:363: AttributeError
+___________________________________ test_plugin_generate_constraints[test_plugin2] ___________________________________
+
+test_plugin = <synthcity.plugins.privacy.plugin_dpgan.DPGANPlugin object at 0x14ff8b6a5d90>
+
+    @pytest.mark.parametrize(
+        "test_plugin", generate_fixtures(plugin_name, plugin, plugin_args)
+    )
+    def test_plugin_generate_constraints(test_plugin: Plugin) -> None:
+        X = pd.DataFrame(load_iris()["data"])
+>       test_plugin.fit(GenericDataLoader(X))
+
+tests/plugins/privacy/test_dpgan.py:81: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py:256: in fit
+    output = self._fit(X, *args, **kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py:287: in _fit
+    self.model.fit(X.dataframe(), cond=cond)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:55: in wrapper_function
+    return vd.call(*args, **kwargs)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:150: in call
+    return self.execute(m)
+/apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/deprecated/decorator.py:222: in execute
+    return self.raw_function(**d, **var_kwargs)
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py:358: in fit
+    self.model.fit(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:278: in fit
+    self._train(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:616: in _train
+    g_loss, d_loss = self._train_epoch(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:493: in _train_epoch
+    self._train_epoch_discriminator(
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py:462: in _train_epoch_discriminator
+    self.discriminator.optimizer.step()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+args = (Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+),)
+kwargs = {}
+self = Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.5, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: None
+    lr: 0.001
+    maximize: False
+    weight_decay: 0.001
+)
+_ = [], profile_name = 'Optimizer.step#DPOptimizer.step'
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+        self, *_ = args
+        self = cast(Optimizer, self)
+        profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+        with torch.autograd.profiler.record_function(profile_name):
+            # call optimizer step pre hooks
+>           for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
+E           AttributeError: 'DPOptimizer' object has no attribute '_optimizer_step_pre_hooks'
+
+/apps/gent/RHEL8/cascadelake-ib/software/PyTorch/2.1.2-foss-2023a/lib/python3.11/site-packages/torch/optim/optimizer.py:363: AttributeError
+_____________________________________ test_plugin_generate_static_cond[source0] ______________________________________
+
+source = <synthcity.utils.datasets.time_series.sine.SineDataloader object at 0x14ff886e43d0>
+
+    @pytest.mark.parametrize(
+        "source", [SineDataloader(with_missing=True), GoogleStocksDataloader()]
+    )
+    def test_plugin_generate_static_cond(source: Any) -> None:
+        static_data, temporal_data, observation_times, outcome = source.load()
+        data = TimeSeriesDataLoader(
+            temporal_data=temporal_data,
+            observation_times=observation_times,
+            static_data=static_data,
+            outcome=outcome,
+        )
+        test_plugin = plugin(
+            n_iter=10,
+        )
+        test_plugin.fit(data)
+    
+        cnt = 50
+        static_seed = static_data.head(1).values.tolist() * cnt
+        X_gen = test_plugin.generate(
+            cnt, static_data=pd.DataFrame(static_seed, columns=static_data.columns)
+        )
+>       static_gen, _, _, _ = X_gen.unpack(as_numpy=True)
+
+tests/plugins/time_series/test_timegan.py:112: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+self =     seq_id  seq_time_id  seq_static_0  seq_static_1  seq_static_2  \
+0        0            0      0.548814      0.7151... -0.140067          1  
+95       -0.017764       -0.080936          1  
+96        0.069294       -0.290315          1  
+as_numpy = True, pad = False
+
+    def unpack(self, as_numpy: bool = False, pad: bool = False) -> Any:
+        if pad:
+            (
+                static_data,
+                temporal_data,
+                observation_times,
+                outcome,
+            ) = TimeSeriesDataLoader.pad_and_mask(
+                self.data["static_data"],
+                self.data["temporal_data"],
+                self.data["observation_times"],
+                self.data["outcome"],
+            )
+        else:
+            static_data, temporal_data, observation_times, outcome = (
+                self.data["static_data"],
+                self.data["temporal_data"],
+                self.data["observation_times"],
+                self.data["outcome"],
+            )
+    
+        if as_numpy:
+            longest_observation_seq = max([len(seq) for seq in temporal_data])
+            return (
+                np.asarray(static_data),
+>               np.asarray(
+                    temporal_data
+                ),  # TODO: check this works with time series benchmarks
+                # masked array to handle variable length sequences
+                ma.vstack(
+                    [
+                        ma.array(
+                            np.resize(ot, longest_observation_seq),
+                            mask=[True for i in range(len(ot))]
+                            + [False for j in range(longest_observation_seq - len(ot))],
+                        )
+                        for ot in observation_times
+                    ]
+                ),
+                np.asarray(outcome),
+            )
+E           ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (50,) + inhomogeneous part.
+
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:936: ValueError
+_____________________________________ test_plugin_generate_static_cond[source1] ______________________________________
+
+source = <synthcity.utils.datasets.time_series.google_stocks.GoogleStocksDataloader object at 0x14ff886e42d0>
+
+    @pytest.mark.parametrize(
+        "source", [SineDataloader(with_missing=True), GoogleStocksDataloader()]
+    )
+    def test_plugin_generate_static_cond(source: Any) -> None:
+        static_data, temporal_data, observation_times, outcome = source.load()
+        data = TimeSeriesDataLoader(
+            temporal_data=temporal_data,
+            observation_times=observation_times,
+            static_data=static_data,
+            outcome=outcome,
+        )
+        test_plugin = plugin(
+            n_iter=10,
+        )
+        test_plugin.fit(data)
+    
+        cnt = 50
+        static_seed = static_data.head(1).values.tolist() * cnt
+        X_gen = test_plugin.generate(
+            cnt, static_data=pd.DataFrame(static_seed, columns=static_data.columns)
+        )
+>       static_gen, _, _, _ = X_gen.unpack(as_numpy=True)
+
+tests/plugins/time_series/test_timegan.py:112: 
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
+
+self =      seq_id  seq_time_id  seq_temporal_Close  seq_temporal_High  \
+0         0     0.985683            0.598831       ...         0.417655  
+473           0.417655  
+474           0.417655  
+475           0.417655  
+476           0.417655  
+as_numpy = True, pad = False
+
+    def unpack(self, as_numpy: bool = False, pad: bool = False) -> Any:
+        if pad:
+            (
+                static_data,
+                temporal_data,
+                observation_times,
+                outcome,
+            ) = TimeSeriesDataLoader.pad_and_mask(
+                self.data["static_data"],
+                self.data["temporal_data"],
+                self.data["observation_times"],
+                self.data["outcome"],
+            )
+        else:
+            static_data, temporal_data, observation_times, outcome = (
+                self.data["static_data"],
+                self.data["temporal_data"],
+                self.data["observation_times"],
+                self.data["outcome"],
+            )
+    
+        if as_numpy:
+            longest_observation_seq = max([len(seq) for seq in temporal_data])
+            return (
+                np.asarray(static_data),
+>               np.asarray(
+                    temporal_data
+                ),  # TODO: check this works with time series benchmarks
+                # masked array to handle variable length sequences
+                ma.vstack(
+                    [
+                        ma.array(
+                            np.resize(ot, longest_observation_seq),
+                            mask=[True for i in range(len(ot))]
+                            + [False for j in range(longest_observation_seq - len(ot))],
+                        )
+                        for ot in observation_times
+                    ]
+                ),
+                np.asarray(outcome),
+            )
+E           ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (50,) + inhomogeneous part.
+
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:936: ValueError
+================================================== warnings summary ==================================================
+../../../../../../../../../apps/gent/RHEL8/cascadelake-ib/software/jupyter-server/2.7.2-GCCcore-12.3.0/lib/python3.11/site-packages/jupyter_client/connect.py:20
+  /apps/gent/RHEL8/cascadelake-ib/software/jupyter-server/2.7.2-GCCcore-12.3.0/lib/python3.11/site-packages/jupyter_client/connect.py:20: DeprecationWarning: Jupyter is migrating its paths to use standard platformdirs
+  given by the platformdirs library.  To remove this warning and
+  see the appropriate new directories, set the environment variable
+  `JUPYTER_PLATFORM_DIRS=1` and then run `jupyter --paths`.
+  The use of platformdirs will be the default in `jupyter_core` v6
+    from jupyter_core.paths import jupyter_data_dir, jupyter_runtime_dir, secure_write
+
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/constraints.py:46: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:47: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:147: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:217: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:225: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:294: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:302: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:309: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:357: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:387: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py:394: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:31: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:37: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:47: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/schema.py:49: 1 warning
+  Pydantic V1 style `@validator` validators are deprecated. You should migrate to Pydantic V2 style `@field_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
+
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/constraints.py:82: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/constraints.py:112: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/constraints.py:142: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/constraints.py:172: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/constraints.py:185: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/feature_encoder.py:63: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/feature_encoder.py:82: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/feature_encoder.py:116: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/compression.py:14: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/compression.py:117: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:304: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:505: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:721: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1025: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1051: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1127: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1156: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1194: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1298: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1326: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1409: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1610: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py:1795: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/core/metric.py:46: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/core/metric.py:72: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/core/metric.py:77: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/convnet.py:69: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/convnet.py:171: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/convnet.py:183: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/convnet.py:202: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/layers.py:55: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/layers.py:122: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/layers.py:145: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/layers.py:155: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/factory.py:113: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/factory.py:128: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/factory.py:134: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mlp.py:24: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mlp.py:51: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mlp.py:111: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mlp.py:244: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mlp.py:256: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mlp.py:275: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py:60: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py:64: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py:121: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py:152: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py:190: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py:245: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py:286: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py:323: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/_utils.py:33: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/serializable.py:77: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/serializable.py:132: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/_base.py:19: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/_base.py:25: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_aft.py:23: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_aft.py:37: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_coxph.py:31: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_coxph.py:48: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_deephit.py:59: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_deephit.py:127: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_xgb.py:94: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_xgb.py:120: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_xgb.py:139: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/_base.py:19: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/_base.py:32: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:66: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:102: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:136: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:174: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:198: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:207: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:266: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:334: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:396: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:410: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:424: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:433: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:448: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:458: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:469: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:483: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:497: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:505: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:525: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:533: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:551: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:605: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py:616: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/samplers.py:23: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/samplers.py:27: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/samplers.py:46: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/samplers.py:91: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/samplers.py:122: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/samplers.py:169: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/samplers.py:176: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_model.py:119: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_model.py:240: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_model.py:278: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_model.py:323: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_model.py:343: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_model.py:692: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_model.py:721: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_dynamic_deephit.py:113: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_dynamic_deephit.py:136: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_dynamic_deephit.py:156: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_coxph.py:74: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_coxph.py:92: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_xgb.py:96: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_xgb.py:112: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_performance.py:153: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_performance.py:236: 1 warning
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+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_deephit.py:129: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_survival_function_regression.py:35: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_survival_function_regression.py:65: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_survival_function_regression.py:70: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_tenn.py:39: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_tenn.py:128: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_tenn.py:406: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_tenn.py:426: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_tenn.py:439: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_xgb.py:94: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_xgb.py:116: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_xgb.py:123: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/_survival_pipeline.py:55: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/plugin_survival_ctgan.py:70: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/plugin_survival_gan.py:77: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/plugin_survae.py:69: 1 warning
+../../../../../../../eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/plugin_survival_nflow.py:69: 1 warning
+tests/plugins/test_plugin_serialization.py: 4 warnings
+  The `validate_arguments` method is deprecated; use `validate_call` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
+
+../../../../../../../../../apps/gent/RHEL8/cascadelake-ib/software/Python/3.11.3-GCCcore-12.3.0/lib/python3.11/site-packages/pkg_resources/__init__.py:121
+  pkg_resources is deprecated as an API
+
+<frozen importlib._bootstrap_external>:629: 43 warnings
+  find_module() is deprecated and slated for removal in Python 3.12; use find_spec() instead
+
+<frozen importlib._bootstrap_external>:1591: 43 warnings
+  FileFinder.find_loader() is deprecated and slated for removal in Python 3.12; use find_spec() instead
+
+<frozen importlib._bootstrap>:283: 43 warnings
+  the load_module() method is deprecated and slated for removal in Python 3.12; use exec_module() instead
+
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/ITK/5.3.0-foss-2023a/lib/python3.11/site-packages/itk/support/types.py:141
+  `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)
+
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+  Please use `shift` from the `scipy.ndimage` namespace, the `scipy.ndimage.interpolation` namespace is deprecated.
+
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+  Please use `binary_erosion` from the `scipy.ndimage` namespace, the `scipy.ndimage.morphology` namespace is deprecated.
+
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+  Please use `distance_transform_edt` from the `scipy.ndimage` namespace, the `scipy.ndimage.morphology` namespace is deprecated.
+
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+  Please use `distance_transform_cdt` from the `scipy.ndimage` namespace, the `scipy.ndimage.morphology` namespace is deprecated.
+
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/MONAI/1.3.0-foss-2023a/lib/python3.11/site-packages/monai/utils/module.py:403
+  Please use `label` from the `scipy.ndimage` namespace, the `scipy.ndimage.measurements` namespace is deprecated.
+
+../../../../../../../../../apps/gent/RHEL8/cascadelake-ib/software/pydantic/2.5.3-GCCcore-12.3.0/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
+  Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
+
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/lightning_fabric/__init__.py:41
+  /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/lightning_fabric/__init__.py:41: Deprecated call to `pkg_resources.declare_namespace('lightning_fabric')`.
+  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
+
+../../../../../../../../../scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/__init__.py:37
+  /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/__init__.py:37: Deprecated call to `pkg_resources.declare_namespace('pytorch_lightning')`.
+  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
+
+tests/benchmarks/test_benchmarks.py::test_benchmark_sanity
+tests/benchmarks/test_benchmarks.py::test_benchmark_sanity
+tests/benchmarks/test_benchmarks.py::test_benchmark_sanity
+tests/benchmarks/test_benchmarks.py::test_benchmark_sanity
+tests/benchmarks/test_benchmarks.py::test_benchmark_sanity
+tests/benchmarks/test_benchmarks.py::test_benchmark_sanity
+tests/metrics/test_sanity.py::test_common_rows[test_plugin0]
+  You are merging on int and float columns where the float values are not equal to their int representation.
+
+tests/benchmarks/test_benchmarks.py: 29 warnings
+  Column mar have very low variance when conditioned on death event present or not. This may harm convergence. This could be a form of 'complete separation'. For example, try the following code:
+  
+  >>> events = df['event'].astype(bool)
+  >>> print(df.loc[events, 'mar'].var())
+  >>> print(df.loc[~events, 'mar'].var())
+  
+  A very low variance means that the column mar completely determines whether a subject dies or not. See https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression.
+
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+  Newton-Raphson convergence completed successfully but norm(delta) is still high, 0.363. This may imply non-unique solutions to the maximum likelihood. Perhaps there is collinearity or complete separation in the dataset?
+
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_workspace_cache
+  Newton-Raphson convergence completed successfully but norm(delta) is still high, 0.341. This may imply non-unique solutions to the maximum likelihood. Perhaps there is collinearity or complete separation in the dataset?
+
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_workspace_cache
+tests/benchmarks/test_benchmarks.py::test_benchmark_workspace_cache
+  Attempting to convert an unexpected datatype 'object' to float. Suggestion: 1) use `lifelines.utils.datetimes_to_durations` to do conversions or 2) manually convert to floats/booleans.
+
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+  Column fin have very low variance when conditioned on death event present or not. This may harm convergence. This could be a form of 'complete separation'. For example, try the following code:
+  
+  >>> events = df['event'].astype(bool)
+  >>> print(df.loc[events, 'fin'].var())
+  >>> print(df.loc[~events, 'fin'].var())
+  
+  A very low variance means that the column fin completely determines whether a subject dies or not. See https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression.
+
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_workspace_cache
+  Newton-Raphson convergence completed successfully but norm(delta) is still high, 0.315. This may imply non-unique solutions to the maximum likelihood. Perhaps there is collinearity or complete separation in the dataset?
+
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_workspace_cache
+  Newton-Raphson convergence completed successfully but norm(delta) is still high, 0.401. This may imply non-unique solutions to the maximum likelihood. Perhaps there is collinearity or complete separation in the dataset?
+
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+tests/benchmarks/test_benchmarks.py::test_benchmark_survival_analysis
+  Newton-Raphson convergence completed successfully but norm(delta) is still high, 0.321. This may imply non-unique solutions to the maximum likelihood. Perhaps there is collinearity or complete separation in the dataset?
+
+tests/benchmarks/test_benchmarks.py: 6 warnings
+tests/metrics/test_api.py: 12 warnings
+tests/metrics/test_performance.py: 24 warnings
+  X does not have valid feature names, but LogisticRegression was fitted with feature names
+
+tests/metrics/test_detection.py: 1 warning
+tests/plugins/core/test_dataloader.py: 4 warnings
+tests/plugins/core/models/test_tabular_encoder.py: 4 warnings
+tests/plugins/core/models/test_transformer.py: 1 warning
+tests/plugins/core/models/test_ts_gan.py: 4 warnings
+tests/plugins/core/models/test_ts_model.py: 36 warnings
+tests/plugins/core/models/test_ts_tabular_gan.py: 2 warnings
+tests/plugins/core/models/test_ts_tabular_vae.py: 1 warning
+tests/plugins/time_series/test_timegan.py: 4 warnings
+tests/plugins/time_series/test_ts_marginal_distributions.py: 4 warnings
+tests/utils/datasets/time_series/test_google_stocks.py: 1 warning
+  The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
+
+tests/metrics/test_performance.py: 12 warnings
+  lbfgs failed to converge (status=1):
+  STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+  
+  Increase the number of iterations (max_iter) or scale the data as shown in:
+      https://scikit-learn.org/stable/modules/preprocessing.html
+  Please also refer to the documentation for alternative solver options:
+      https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+
+tests/metrics/test_privacy.py: 48 warnings
+tests/plugins/privacy/test_privbayes.py: 25 warnings
+tests/utils/test_anonymization.py: 208 warnings
+  The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
+
+tests/metrics/test_privacy.py::test_evaluator[test_plugin0-DomiasMIABNAF]
+  This overload of add_ is deprecated:
+  	add_(Number alpha, Tensor other)
+  Consider using one of the following signatures instead:
+  	add_(Tensor other, *, Number alpha) (Triggered internally at /tmp/vsc40003/easybuild/PyTorch/2.1.2/foss-2023a/pytorch-v2.1.2/torch/csrc/utils/python_arg_parser.cpp:1519.)
+
+tests/metrics/test_statistical.py: 2 warnings
+tests/plugins/test_plugin_serialization.py: 2 warnings
+tests/plugins/core/models/test_tabular_encoder.py: 110 warnings
+tests/plugins/core/models/test_tabular_gan.py: 85 warnings
+tests/plugins/core/models/test_tabular_vae.py: 230 warnings
+tests/plugins/core/models/test_ts_tabular_gan.py: 23 warnings
+tests/plugins/core/models/test_ts_tabular_vae.py: 12 warnings
+tests/plugins/domain_adaptation/test_radialgan.py: 54 warnings
+tests/plugins/generic/test_bayesian_network.py: 52 warnings
+tests/plugins/generic/test_ctgan.py: 44 warnings
+tests/plugins/generic/test_nflow.py: 10 warnings
+tests/plugins/generic/test_rtvae.py: 18 warnings
+tests/plugins/generic/test_tvae.py: 32 warnings
+tests/plugins/privacy/test_adsgan.py: 9 warnings
+tests/plugins/privacy/test_decaf.py: 6 warnings
+tests/plugins/privacy/test_dpgan.py: 24 warnings
+tests/plugins/privacy/test_pategan.py: 11 warnings
+tests/plugins/survival_analysis/test_survae.py: 3 warnings
+tests/plugins/survival_analysis/test_survival_ctgan.py: 7 warnings
+tests/plugins/time_series/test_timegan.py: 52 warnings
+  Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.
+
+tests/metrics/test_statistical.py: 1 warning
+tests/plugins/test_plugin_serialization.py: 1 warning
+tests/plugins/core/models/test_tabular_gan.py: 42 warnings
+tests/plugins/core/models/test_tabular_vae.py: 113 warnings
+tests/plugins/core/models/test_ts_tabular_gan.py: 10 warnings
+tests/plugins/core/models/test_ts_tabular_vae.py: 6 warnings
+tests/plugins/domain_adaptation/test_radialgan.py: 15 warnings
+tests/plugins/generic/test_ctgan.py: 13 warnings
+tests/plugins/generic/test_rtvae.py: 9 warnings
+tests/plugins/generic/test_tvae.py: 10 warnings
+tests/plugins/privacy/test_adsgan.py: 4 warnings
+tests/plugins/privacy/test_decaf.py: 3 warnings
+tests/plugins/privacy/test_dpgan.py: 12 warnings
+tests/plugins/privacy/test_pategan.py: 5 warnings
+tests/plugins/survival_analysis/test_survae.py: 3 warnings
+tests/plugins/survival_analysis/test_survival_ctgan.py: 7 warnings
+tests/plugins/survival_analysis/test_survival_gan.py: 17 warnings
+tests/plugins/time_series/test_timegan.py: 20 warnings
+  Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.
+
+tests/plugins/core/models/test_convnet.py: 7 warnings
+tests/plugins/core/models/test_mlp.py: 1 warning
+tests/plugins/images/test_image_adsgan.py: 2 warnings
+tests/plugins/images/test_image_cgan.py: 2 warnings
+  Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
+
+tests/plugins/core/models/test_gan.py: 1 warning
+tests/plugins/core/models/test_tabular_gan.py: 1 warning
+tests/plugins/privacy/test_dpgan.py: 12 warnings
+  Secure RNG turned off. This is perfectly fine for experimentation as it allows for much faster training performance, but remember to turn it on and retrain one last time before production with ``secure_mode`` turned on.
+
+tests/plugins/core/models/test_gan.py: 1 warning
+tests/plugins/core/models/test_tabular_gan.py: 1 warning
+tests/plugins/privacy/test_dpgan.py: 12 warnings
+  Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.
+
+tests/plugins/core/models/test_gan.py: 1 warning
+tests/plugins/core/models/test_tabular_gan.py: 1 warning
+tests/plugins/privacy/test_dpgan.py: 12 warnings
+  Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.
+
+tests/plugins/core/models/test_normalizing_flows.py::test_nf_fit_generate[affine-coupling-lu-standard_normal]
+  torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.
+  torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.
+  X = torch.triangular_solve(B, A).solution
+  should be replaced with
+  X = torch.linalg.solve_triangular(A, B). (Triggered internally at /tmp/vsc40003/easybuild/PyTorch/2.1.2/foss-2023a/pytorch-v2.1.2/aten/src/ATen/native/BatchLinearAlgebra.cpp:2192.)
+
+tests/plugins/core/models/test_ts_gan.py: 36 warnings
+tests/plugins/core/models/test_ts_model.py: 9 warnings
+  enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)
+
+tests/plugins/core/models/test_ts_gan.py: 36 warnings
+tests/plugins/core/models/test_ts_model.py: 9 warnings
+  torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+
+tests/plugins/core/models/test_ts_gan.py: 4 warnings
+tests/plugins/core/models/test_ts_tabular_gan.py: 2 warnings
+tests/plugins/core/models/test_ts_tabular_vae.py: 1 warning
+tests/plugins/time_series/test_timegan.py: 4 warnings
+  Initializing zero-element tensors is a no-op
+
+tests/plugins/core/models/dag/test_dag_sanity.py::test_sanity
+tests/plugins/privacy/test_decaf.py::test_get_dag[d-struct]
+  /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.
+
+tests/plugins/core/models/time_series_survival/test_ts_surv_coxph.py::test_train_prediction_coxph[MLP-Transformer]
+  Column(s) [9, 25, 29, 35] have very low variance. This may harm convergence. 1) Are you using formula's? Did you mean to add '-1' to the end. 2) Try dropping this redundant column before fitting if convergence fails.
+
+tests/plugins/core/models/time_series_survival/test_ts_surv_coxph.py::test_train_prediction_coxph[MLP-Transformer]
+  Column(s) [6, 9, 10, 18, 20, 32] have very low variance. This may harm convergence. 1) Are you using formula's? Did you mean to add '-1' to the end. 2) Try dropping this redundant column before fitting if convergence fails.
+
+tests/plugins/core/models/time_series_survival/test_ts_surv_coxph.py::test_train_prediction_coxph[MLP-Transformer]
+  Column(s) [4, 7, 36, 38] have very low variance. This may harm convergence. 1) Are you using formula's? Did you mean to add '-1' to the end. 2) Try dropping this redundant column before fitting if convergence fails.
+
+tests/plugins/generic/test_arf.py: 27 warnings
+  Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable OOB estimates.
+
+tests/plugins/generic/test_great.py: 13 warnings
+  Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: 
+  dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
+
+tests/plugins/privacy/test_aim.py: 96 warnings
+  in the future the `.dtype` attribute of a given datatype object must be a valid dtype instance. `data_type.dtype` may need to be coerced using `np.dtype(data_type.dtype)`. (Deprecated NumPy 1.20)
+
+tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin0]
+tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin1]
+tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin2]
+  /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
+
+tests/plugins/privacy/test_decaf.py::test_get_dag[d-struct]
+  /scratch/gent/vo/001/gvo00117/easybuild/RHEL8/cascadelake-ampere-ib/software/PyTorch-Lightning/2.2.1-foss-2023a/lib/python3.11/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:653: Checkpoint directory /tmp/vsc47063/easybuild/build/synthcity/0.2.10/foss-2023a/synthcity/synthcity-0.2.10/lightning_logs/version_20076309/checkpoints exists and is not empty.
+
+tests/utils/test_anonymization.py::test_k_anonymity_anonymize_full
+tests/utils/test_anonymization.py::test_l_diversity_anonymize_full
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.2]
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.5]
+  Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.
+
+tests/utils/test_anonymization.py::test_k_anonymity_anonymize_full
+tests/utils/test_anonymization.py::test_l_diversity_anonymize_full
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.2]
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.5]
+  Number of distinct clusters (1) found smaller than n_clusters (5). Possibly due to duplicate points in X.
+
+tests/utils/test_anonymization.py::test_k_anonymity_anonymize_full
+tests/utils/test_anonymization.py::test_l_diversity_anonymize_full
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.2]
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.5]
+  Number of distinct clusters (1) found smaller than n_clusters (10). Possibly due to duplicate points in X.
+
+tests/utils/test_anonymization.py::test_k_anonymity_anonymize_full
+tests/utils/test_anonymization.py::test_l_diversity_anonymize_full
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.2]
+tests/utils/test_anonymization.py::test_t_closeness_anonymize_full[0.5]
+  Number of distinct clusters (1) found smaller than n_clusters (15). Possibly due to duplicate points in X.
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+
+---------- coverage: platform linux, python 3.11.3-final-0 -----------
+Name                                                                                                                                                 Stmts   Miss  Cover   Missing
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/__init__.py                                                                16      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/benchmark/__init__.py                                                     184     45    76%   116, 120, 125, 209-212, 228-231, 242, 266, 278-283, 354-390
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/benchmark/utils.py                                                         55      1    98%   79
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/logger.py                                                                  53     18    66%   48-57, 67-73, 79, 103
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/__init__.py                                                         2      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/_utils.py                                                         230     34    85%   63, 82, 101, 147-149, 152, 157-158, 161, 172, 176, 180, 192, 196, 204-220, 254, 349-353, 428, 466
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/core/__init__.py                                                    1      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/core/metric.py                                                     71      7    90%   75, 80, 85, 90, 95, 109, 119
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval.py                                                            71     13    82%   168, 173, 175, 177, 179, 182, 189, 191-192, 194-198, 201, 235
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_attacks.py                                                    84      6    93%   54, 57, 62, 76-77, 94
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_detection.py                                                 138     32    77%   58, 62, 192-243, 248
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_performance.py                                               473    181    62%   71, 147-149, 169, 250, 253, 359-475, 494, 562, 590, 631, 637, 670, 713, 764, 797-811, 819-873, 882, 888, 909-917, 934, 944, 964, 986-991, 999, 1006-1011, 1019-1150, 1158-1160
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_privacy.py                                                   266     22    92%   45, 56, 88, 97, 109, 138, 146, 162, 189, 194, 201, 210, 234, 239, 246, 256, 294, 334, 405, 409, 437, 486
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_sanity.py                                                    121     12    90%   35-37, 53, 57, 65, 94, 100, 138, 166, 200, 236
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/eval_statistical.py                                               371     29    92%   48, 57, 230-233, 255-285, 334, 385, 576, 635, 642, 780, 784, 826, 875, 878, 887-888, 893-894, 908
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/plots.py                                                           49      1    98%   77
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/representations/OneClass.py                                        75     13    83%   20-23, 58, 65, 103, 146-150, 182-193, 198
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/representations/__init__.py                                         0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/representations/networks.py                                        24      2    92%   70, 85
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/scores.py                                                          67      5    93%   36-39, 45
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/metrics/weighted_metrics.py                                                60      8    87%   43, 88, 91-92, 94-95, 97-98
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/__init__.py                                                        16      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/__init__.py                                                    0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/constraints.py                                               119     18    85%   75, 78, 110, 125-140, 156-157, 277, 284, 288-290, 296, 300-302
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataloader.py                                                743     81    89%   99, 103, 111, 116, 120, 124, 132, 136, 140, 144, 149, 153, 157, 161, 165, 169, 173, 182, 186, 190, 218-221, 241-243, 256, 264, 268, 326, 371, 424, 440, 465-466, 520, 523, 531, 573, 583, 645, 661, 685-686, 740, 747, 750, 754, 757, 809, 816, 885-886, 960, 963, 1005-1014, 1040, 1044, 1088, 1092, 1122, 1453, 1467, 1471, 1663, 1712, 1752, 1755, 1761, 1767, 1783, 1786, 1789, 1792, 1811
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/dataset.py                                                    95     15    84%   71-73, 86, 110, 128-131, 150, 176-179, 182-185, 188
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/distribution.py                                              264     23    91%   54, 63, 69, 90, 95, 100, 105, 110, 115, 120, 132, 136, 154, 175, 205, 273, 279, 312, 360, 364, 370, 408, 461
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/RGCNConv.py                                           111    111     0%   2-278
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/__init__.py                                             0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/aim.py                                                174     23    87%   11, 72, 74, 76, 106, 108, 111, 135, 193-196, 203, 354-365
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/bnaf.py                                               181     33    82%   80, 84, 87, 99, 124, 128, 144, 262, 311, 313, 315, 317, 319, 332-334, 344, 349, 352, 380, 391, 418-420, 454-455, 458-459, 462-466
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/convnet.py                                            196     29    85%   28, 31-34, 90, 121, 162, 173-181, 193, 196-200, 220, 241, 244, 292-293, 355-356, 428, 473, 553, 584
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/dag/__init__.py                                         0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/dag/data.py                                            51      4    92%   20, 33, 48, 51
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/dag/dsl.py                                            149     51    66%   38, 40, 61, 75, 77, 133-141, 145-155, 158-165, 168-171, 174-182, 185-186, 189-190, 193-198, 202-203
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/dag/dstruct.py                                        145      2    99%   116, 169
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/dag/utils.py                                          146     14    90%   50, 62, 64, 84, 98, 107, 169, 193, 218-221, 224, 226
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/factory.py                                             45      4    91%   92, 99, 107, 150
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/feature_encoder.py                                    167     11    93%   26, 32, 35, 59, 61, 72, 88, 100, 112, 175, 178
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/flows.py                                              152     16    89%   181, 191, 201, 207-212, 245, 261, 270, 278, 290-298, 396
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/functions.py                                           72     54    25%   14-18, 50-56, 60-68, 91-99, 111-120, 124-130, 136-152
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/gan.py                                                269     10    96%   172, 264, 268, 272, 306, 314, 384, 467, 529, 669
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/goggle.py                                             367    363     1%   8-730
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/image_gan.py                                          268     54    80%   24-32, 39-40, 159, 313, 337, 396-401, 416, 467, 476-501, 510, 553-558, 587-588, 596, 601-612, 615, 641
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/layers.py                                              91     11    88%   28-29, 32, 42, 52, 142-143, 147, 152-153, 157
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/__init__.py                                         0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/callbacks.py                                       75     61    19%   23-25, 28, 31-35, 51-53, 56-64, 67-77, 80-96, 99-113
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/clique_vector.py                                   61     20    67%   27-29, 34-36, 41-43, 50-52, 59-65, 87, 96-97, 100-101, 107
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/dataset.py                                         41     14    66%   21, 23, 35-38, 47-50, 55, 61-62, 66
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/domain.py                                          57     14    75%   13, 21, 31, 54, 87-91, 104, 114, 117-118, 121
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/factor.py                                         155     50    68%   17, 19, 21, 25, 35, 39, 43, 53-56, 60, 70, 82, 86-87, 107-110, 113-118, 124-127, 132-133, 161-166, 183-190, 199-202
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/factor_graph.py                                   222    222     0%   2-287
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/graphical_model.py                                212     85    60%   44-50, 54, 58, 90-101, 114-157, 161-164, 188, 203-210, 214-223, 236-237, 263-266, 269, 292-300
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/identity.py                                       209    122    42%   16-29, 46-48, 51-52, 55, 64, 71, 75, 80-83, 86, 89, 92, 95, 101-110, 116-118, 121, 124-126, 136-139, 145-147, 153-155, 160, 163, 166-168, 175-178, 181-182, 185, 188, 191-192, 195-197, 201, 204, 207, 215-217, 220, 223, 226-230, 233, 237-242, 249-255, 258, 261, 264, 267, 270, 273, 276, 279, 283, 288-293, 296, 299, 303, 306, 309, 326, 330, 334, 337, 340, 343, 346
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/inference.py                                      231    105    55%   73-75, 102, 105-108, 113, 115, 119, 121, 123, 125, 127, 129, 131, 154-184, 202-234, 253, 261, 265-266, 276, 304, 318-320, 346, 375-385, 400-411, 415-419
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/junction_tree.py                                   91      9    90%   90-95, 116-120
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/local_inference.py                                153    153     0%   2-293
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/mechanism.py                                       48     48     0%   2-96
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/mixture_inference.py                              139    139     0%   2-206
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/public_inference.py                                82     82     0%   2-128
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/region_graph.py                                   371    371     0%   2-544
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mbi/torch_factor.py                                   153    153     0%   2-206
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/mlp.py                                                160      6    96%   142, 144, 198, 229, 271, 287
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/__init__.py                           6      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/_base.py                             32      4    88%   22, 28, 34, 40
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/benchmarks.py                       109     48    56%   65, 95-96, 129-152, 155-190, 235-263
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/loader.py                            21     10    52%   33-45, 63
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/metrics.py                           69      4    94%   89, 118, 131, 133
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_aft.py                          41      1    98%   52
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_coxph.py                        46      1    98%   66
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_deephit.py                      69      1    99%   64
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/surv_xgb.py                          69     21    70%   59, 88, 107, 113, 116-118, 123-135, 141-143
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/third_party/__init__.py               0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/third_party/metrics.py              122     19    84%   22, 34-52, 60, 77, 128, 206-212, 295
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/third_party/nonparametric.py        131     46    65%   126-172, 244-249, 263-265, 291-297, 320, 360-361, 390
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/survival_analysis/third_party/util.py                  81     52    36%   43-70, 90-93, 136, 148, 153, 158-159, 163, 199-203, 251-280
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabnet.py                                             199    199     0%   7-516
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_aim.py                                         41      3    93%   93, 98, 102
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_arf.py                                         47      6    87%   13-14, 145-150
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_ddpm/__init__.py                              105      1    99%   94
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_ddpm/gaussian_multinomial_diffsuion.py        441    163    63%   41-44, 64, 88, 92, 94, 97, 114, 197, 236-239, 245, 247, 258, 273, 288, 304-307, 316, 335-358, 375-381, 398-399, 413, 422, 485, 489, 528-531, 584, 601-612, 621, 641-647, 690-765, 785-812, 821-834, 840-867, 871-900, 943-944, 955
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_ddpm/modules.py                                63      8    87%   39, 83, 91-92, 101-102, 110, 112
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_ddpm/utils.py                                  75     31    59%   18-32, 46, 64-84, 130, 153-154, 159-170
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_encoder.py                                    265     38    86%   34, 44, 50, 154, 182-185, 222-226, 260-264, 317-324, 454, 529, 564, 576-589, 600-603, 613-614, 623
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_flows.py                                       35      2    94%   144, 163
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_gan.py                                        145     39    73%   182, 236-237, 240, 255, 260, 353, 377-380, 409-435, 440-460
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_goggle.py                                      66     58    12%   16-247
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_great.py                                       29      2    93%   13-14
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/tabular_vae.py                                         96      7    93%   173-174, 177, 192, 195, 245, 268
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/__init__.py                        6      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/_base.py                          32      4    88%   29, 41, 47, 53
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/benchmarks.py                    164     94    43%   28-39, 43-51, 65-82, 100-139, 195, 217, 228-229, 253-288, 354-450
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/loader.py                          9      1    89%   20
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_coxph.py                  34      1    97%   51
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_dynamic_deephit.py       296     22    93%   67, 69, 95, 298, 319, 324, 407, 427, 435-438, 460, 556, 560, 622, 644, 697, 725, 733, 738, 759
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/ts_surv_xgb.py                    35      5    86%   60-61, 110, 122-123
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_series_survival/utils.py                          73     52    29%   55-59, 74-85, 88, 91-98, 132-173
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/__init__.py                               3      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/_base.py                                 45      5    89%   23, 29, 35, 41, 47
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/benchmarks.py                            95     78    18%   27-30, 34-35, 47-101, 105-119, 131-146, 167-214
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/loader.py                                13      1    92%   28
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/metrics.py                               60     13    78%   28, 31, 38, 73-85
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_aft.py                               38      6    84%   54-62
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_coxph.py                             38      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_date.py                             188     13    93%   82, 261, 268, 302, 307, 380-383, 438-445
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_deephit.py                           67      7    90%   48, 133-140
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_survival_function_regression.py      47      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_survival_time_series.py              43     13    70%   79-105, 115, 129-138
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_tenn.py                             197     30    85%   188, 260-263, 284, 299, 311, 336-353, 362, 371, 382, 391, 402, 443-450
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/time_to_event/tte_xgb.py                               61     17    72%   48, 59, 87-90, 106, 112-114, 120-121, 127-134
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/transformer.py                                         12      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_gan.py                                             227      7    97%   336, 340, 344, 404, 412, 583, 640
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_model.py                                           272     16    94%   155, 157, 159, 164, 224, 251, 253, 255, 266, 378, 380, 484, 641, 679, 686, 726
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_tabular_gan.py                                      66      8    88%   157, 245, 259, 262, 274-276, 323
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_tabular_vae.py                                      61     10    84%   128, 199, 206, 213, 220, 223, 234-236, 271
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/ts_vae.py                                             162      7    96%   35-37, 52, 510, 544, 593
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/models/vae.py                                                239     10    96%   231, 289, 293, 297, 318, 326, 526, 536, 538, 591
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/plugin.py                                                    331     59    82%   89, 93, 122, 140-154, 160, 166, 171, 239-248, 273, 327, 330, 337, 355, 365, 394, 431, 514, 521, 544-549, 597, 599, 613-615, 618-619, 628, 645-646, 655, 670, 673, 708, 723, 729, 735-737, 741, 745
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/schema.py                                                    103     10    90%   62, 65, 89, 112-122, 139, 162, 203
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/core/serializable.py                                               79      7    91%   44, 79, 85, 96, 102, 105, 123
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/domain_adaptation/__init__.py                                       0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/domain_adaptation/plugin_radialgan.py                             291      7    98%   121, 256, 329, 373, 459, 515, 875
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/__init__.py                                                 8      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_arf.py                                              41      2    95%   160, 168
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_bayesian_network.py                                 68      1    99%   164
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_ctgan.py                                            75      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_ddpm.py                                             69      4    94%   209, 213, 225, 244
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_dummy_sampler.py                                    33      1    97%   62
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_goggle.py                                           65     36    45%   34, 156-187, 191, 195, 199, 214-247, 250-255, 261
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_great.py                                            42      2    95%   161, 187
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_marginal_distributions.py                           30      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_nflow.py                                            71      3    96%   244, 285-286
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_rtvae.py                                            59      2    97%   191, 230
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_tvae.py                                             60      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/generic/plugin_uniform_sampler.py                                  30      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/images/__init__.py                                                  8      8     0%   2-16
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/images/plugin_image_adsgan.py                                      96      4    96%   212, 219, 313, 329
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/images/plugin_image_cgan.py                                        95      2    98%   206, 322
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/__init__.py                                                 8      8     0%   2-16
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_adsgan.py                                           69      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_aim.py                                              41      2    95%   151, 159
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_decaf.py                                           146     37    75%   263, 268-275, 298-300, 313-339, 350-356, 439, 448-468
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_dpgan.py                                            71      2    97%   240, 294
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_pategan.py                                         160      4    98%   61-62, 278, 284
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/privacy/plugin_privbayes.py                                       301     31    90%   58, 161-166, 173, 177-184, 213, 247-248, 334, 336, 369, 372, 374, 392, 395, 409-412, 437, 439, 468, 510, 520-521
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/__init__.py                                       8      1    88%   13
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/_survival_pipeline.py                            87      6    93%   108, 118, 138, 153, 190, 211
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/plugin_survae.py                                 48      2    96%   98, 140
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/plugin_survival_ctgan.py                         48      2    96%   99, 141
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/plugin_survival_gan.py                           75      3    96%   105, 112, 158
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/survival_analysis/plugin_survival_nflow.py                         47      8    83%   98, 130, 133-150, 153
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/time_series/__init__.py                                             8      8     0%   2-16
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/time_series/plugin_fflows.py                                       78     43    45%   170-225, 228-289
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/time_series/plugin_timegan.py                                     124      5    96%   267, 273, 383, 394, 435
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/plugins/time_series/plugin_timevae.py                                      82     28    66%   205, 233-312, 315-344
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/anonymization.py                                                    175     10    94%   32, 36, 40, 68-70, 76, 124, 147, 159
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/callbacks.py                                                        116     12    90%   87-88, 119, 141, 153, 160, 166-167, 170-173
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/compression.py                                                      109      9    92%   59-60, 122, 125, 128, 136, 146, 158, 181
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/constants.py                                                          2      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/dataframe.py                                                          5      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/datasets/__init__.py                                                  0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/datasets/categorical/__init__.py                                      0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/datasets/categorical/categorical_adult.py                            20      1    95%   32
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/datasets/time_series/__init__.py                                      0      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/datasets/time_series/google_stocks.py                                43      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/datasets/time_series/pbc.py                                          78      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/datasets/time_series/sine.py                                         36      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/evaluation.py                                                        49      0   100%
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/optimizer.py                                                        114     94    18%   35-121, 136-144, 147-155, 161-162, 168-176, 179-183, 209-232
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/redis_wrapper.py                                                     13      5    62%   18-21, 24, 27
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/reproducibility.py                                                   15      4    73%   13-14, 23-24
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/samplers.py                                                         157     20    87%   25, 29, 33, 37, 40, 82, 142-151, 171-174, 188, 201, 218, 245, 294
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/utils/serialization.py                                                     26      4    85%   24, 42-45
+/tmp/vsc47063/eb-ral4agm2/tmpms13kdhu/lib/python3.11/site-packages/synthcity/version.py                                                                  3      0   100%
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+TOTAL                                                                                                                                                17694   4691    73%
+
+================================================ slowest 50 durations ================================================
+1428.63s call     tests/plugins/generic/test_great.py::test_eval_performance_great[False]
+1426.69s call     tests/plugins/generic/test_great.py::test_eval_performance_great[True]
+1004.85s call     tests/plugins/generic/test_great.py::test_plugin_generate[True-test_plugin0]
+1004.37s call     tests/plugins/generic/test_great.py::test_plugin_generate[True-test_plugin1]
+1004.25s call     tests/plugins/generic/test_great.py::test_plugin_generate[True-test_plugin2]
+989.97s call     tests/plugins/generic/test_great.py::test_plugin_generate[False-test_plugin1]
+989.96s call     tests/plugins/generic/test_great.py::test_plugin_generate[False-test_plugin0]
+977.21s call     tests/plugins/generic/test_great.py::test_plugin_generate[False-test_plugin2]
+630.52s call     tests/plugins/generic/test_great.py::test_plugin_fit[test_plugin0]
+627.75s call     tests/plugins/generic/test_great.py::test_plugin_fit[test_plugin2]
+624.23s call     tests/plugins/generic/test_great.py::test_plugin_fit[test_plugin1]
+210.08s call     tests/metrics/test_performance.py::test_evaluate_performance_regression[PerformanceEvaluatorMLP-test_plugin0]
+184.00s call     tests/plugins/privacy/test_aim.py::test_plugin_encoding
+108.05s call     tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin5]
+107.90s call     tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin3]
+107.66s call     tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin4]
+103.22s call     tests/plugins/time_series/test_timegan.py::test_plugin_generate_survival_sampler[none]
+95.12s call     tests/plugins/images/test_image_cgan.py::test_plugin_generate
+94.97s call     tests/plugins/images/test_image_adsgan.py::test_plugin_generate
+93.86s call     tests/plugins/images/test_image_cgan.py::test_plugin_generate_with_conditional
+89.67s call     tests/plugins/core/models/test_convnet.py::test_train_clf
+81.76s call     tests/plugins/survival_analysis/test_survival_gan.py::test_plugin_generate_with_conditional
+72.56s call     tests/plugins/time_series/test_timegan.py::test_plugin_generate_survival_sampler[imbalanced_censoring]
+72.34s call     tests/plugins/time_series/test_timegan.py::test_plugin_generate_survival_sampler[imbalanced_time_censoring]
+62.26s call     tests/plugins/survival_analysis/test_survae.py::test_plugin_generate_with_conditional_survae
+57.75s call     tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin0]
+57.44s call     tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[XceptionTime]
+54.74s call     tests/plugins/generic/test_tvae.py::test_plugin_conditional
+50.53s call     tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin1]
+50.11s call     tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin2]
+47.79s call     tests/plugins/images/test_image_adsgan.py::test_plugin_fit
+47.74s call     tests/metrics/test_privacy.py::test_evaluator[test_plugin0-DomiasMIABNAF]
+45.94s call     tests/plugins/survival_analysis/test_survival_ctgan.py::test_plugin_generate_with_conditional
+45.37s call     tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[TCN]
+43.18s call     tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin1]
+42.82s call     tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin2]
+42.67s call     tests/plugins/generic/test_ctgan.py::test_plugin_conditional_ctgan
+42.67s call     tests/plugins/generic/test_ddpm.py::test_plugin_generate_constraints[test_plugin0]
+35.66s call     tests/metrics/test_statistical.py::test_evaluate_prdc[test_plugin0]
+34.02s call     tests/plugins/core/models/test_tabular_vae.py::test_vae_classification[robust_divergence]
+33.50s call     tests/plugins/privacy/test_adsgan.py::test_plugin_generate
+33.50s call     tests/plugins/core/models/test_tabular_vae.py::test_vae_classification_early_stopping[robust_divergence]
+32.96s call     tests/metrics/test_performance.py::test_evaluate_performance_time_series_survival[PerformanceEvaluatorLinear-test_plugin0]
+32.60s call     tests/plugins/core/models/test_tabular_vae.py::test_vae_classification[standard]
+32.46s call     tests/plugins/privacy/test_adsgan.py::test_plugin_conditional_adsgan
+31.93s call     tests/metrics/test_performance.py::test_evaluate_performance_time_series_survival[PerformanceEvaluatorMLP-test_plugin0]
+31.32s call     tests/plugins/core/models/test_tabular_vae.py::test_vae_classification_early_stopping[standard]
+27.31s call     tests/metrics/test_statistical.py::test_image_support
+27.30s call     tests/plugins/core/models/test_ts_model.py::test_rnn_irregular_ts[OmniScaleCNN]
+26.37s call     tests/plugins/core/models/test_image_gan.py::test_image_gan_generation[generator_extra_penalties0]
+============================================== short test summary info ===============================================
+FAILED tests/metrics/test_performance.py::test_evaluate_performance_time_series_survival[PerformanceEvaluatorXGB-test_plugin0]
+FAILED tests/plugins/core/test_schema.py::test_schema_fail - AttributeError: module 'pydantic' has no attribute 'py...
+FAILED tests/plugins/core/models/survival_analysis/test_surv_xgb.py::test_train_prediction - AttributeError: `best_...
+FAILED tests/plugins/core/models/time_series_survival/test_ts_surv_xgb.py::test_train_prediction[Transformer] - Att...
+FAILED tests/plugins/core/models/time_to_event/test_tte_xgb.py::test_train_prediction - AttributeError: `best_itera...
+FAILED tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin0] - RuntimeError: Training with multip...
+FAILED tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin1] - RuntimeError: Training with multip...
+FAILED tests/plugins/privacy/test_decaf.py::test_plugin_generate[test_plugin2] - RuntimeError: Training with multip...
+FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin1] - AttributeError: 'DPOptimizer'...
+FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[True-test_plugin2] - AttributeError: 'DPOptimizer'...
+FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin0] - AttributeError: 'DPOptimizer...
+FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin1] - AttributeError: 'DPOptimizer...
+FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate[False-test_plugin2] - AttributeError: 'DPOptimizer...
+FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin0] - AttributeError: 'DPOpt...
+FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin1] - AttributeError: 'DPOpt...
+FAILED tests/plugins/privacy/test_dpgan.py::test_plugin_generate_constraints[test_plugin2] - AttributeError: 'DPOpt...
+FAILED tests/plugins/time_series/test_timegan.py::test_plugin_generate_static_cond[source0] - ValueError: setting a...
+FAILED tests/plugins/time_series/test_timegan.py::test_plugin_generate_static_cond[source1] - ValueError: setting a...
+======== 18 failed, 2141 passed, 13 skipped, 103 deselected, 2 xfailed, 2316 warnings in 15511.26s (4:18:31) =========
+pytest-xprocess reminder::Be sure to terminate the started process by running 'pytest --xkill' if you have not explicitly done so in your fixture with 'xprocess.getinfo(<process_name>).terminate()'.