🤖 Lint code

ivy/data_classes/array/experimental/layers.py

@@ -1336,6 +1336,7 @@ def adaptive_max_pool3d(
         """
         Apply a 3D adaptive maximum pooling over an input signal composed of several
         input planes.
+
         Parameters
         ----------
         self

ivy/data_classes/container/experimental/layers.py

@@ -2869,6 +2869,7 @@ def static_adaptive_max_pool3d(
         ivy.Container static method variant of ivy.adaptive_max_pool3d. This method
         simply wraps the function, and so the docstring for ivy.adaptive_max_pool3d also
         applies to this method with minimal changes.
+
         Parameters
         ----------
         input
@@ -2906,6 +2907,7 @@ def adaptive_max_pool3d(
         """
         Apply a 3D adaptive maximum pooling over an input signal composed of several
         input planes.
+
         Parameters
         ----------
         self

ivy/functional/ivy/experimental/layers.py

@@ -3232,6 +3232,7 @@ def adaptive_max_pool3d(
     """
     Apply a 3D adaptive maximum pooling over an input signal composed of several input
     planes.
+
     Parameters
     ----------
     input

ivy_tests/test_ivy/test_functional/test_experimental/test_nn/test_layers.py

@@ -583,6 +583,44 @@ def test_adaptive_max_pool2d(
     )
 
 
+@handle_test(
+    fn_tree="functional.ivy.experimental.adaptive_max_pool3d",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_num_dims=4,
+        max_num_dims=5,
+        min_dim_size=1,
+        # Setting max and min value because this operation in paddle is not
+        # numerically stable
+        max_value=100,
+        min_value=-100,
+    ),
+    output_size=st.one_of(
+        st.tuples(
+            helpers.ints(min_value=1, max_value=5),
+            helpers.ints(min_value=1, max_value=5),
+            helpers.ints(min_value=1, max_value=5),
+        ),
+        helpers.ints(min_value=1, max_value=5),
+    ),
+    test_with_out=st.just(False),
+    ground_truth_backend="torch",
+)
+def test_adaptive_max_pool3d(
+    *, dtype_and_x, output_size, test_flags, backend_fw, fn_name, on_device
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_function(
+        input_dtypes=input_dtype,
+        test_flags=test_flags,
+        backend_to_test=backend_fw,
+        on_device=on_device,
+        fn_name=fn_name,
+        input=x[0],
+        output_size=output_size,
+    )
+
+
 @handle_test(
     fn_tree="functional.ivy.experimental.avg_pool1d",
     x_k_s_p=helpers.arrays_for_pooling(min_dims=3, max_dims=3, min_side=1, max_side=4),
@@ -1437,41 +1475,3 @@ def test_stft(
         window_fn=None,
         pad_end=True,
     )
-
-
-@handle_test(
-    fn_tree="functional.ivy.experimental.adaptive_max_pool3d",
-    dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("float"),
-        min_num_dims=4,
-        max_num_dims=5,
-        min_dim_size=1,
-        # Setting max and min value because this operation in paddle is not
-        # numerically stable
-        max_value=100,
-        min_value=-100,
-    ),
-    output_size=st.one_of(
-        st.tuples(
-            helpers.ints(min_value=1, max_value=5),
-            helpers.ints(min_value=1, max_value=5),
-            helpers.ints(min_value=1, max_value=5),
-        ),
-        helpers.ints(min_value=1, max_value=5),
-    ),
-    test_with_out=st.just(False),
-    ground_truth_backend="torch",
-)
-def test_adaptive_max_pool3d(
-    *, dtype_and_x, output_size, test_flags, backend_fw, fn_name, on_device
-):
-    input_dtype, x = dtype_and_x
-    helpers.test_function(
-        input_dtypes=input_dtype,
-        test_flags=test_flags,
-        backend_to_test=backend_fw,
-        on_device=on_device,
-        fn_name=fn_name,
-        input=x[0],
-        output_size=output_size,
-    )