diff --git a/tutorials/sphinx-tutorials/export.py b/tutorials/sphinx-tutorials/export.py
index 09c6ca5ccd5..3cef4a7115a 100644
--- a/tutorials/sphinx-tutorials/export.py
+++ b/tutorials/sphinx-tutorials/export.py
@@ -338,51 +338,27 @@
 # `AOTI documentation <https://pytorch.org/docs/main/torch.compiler_aot_inductor.html>`_:
 #
 
-from tempfile import TemporaryDirectory
-
-from torch._inductor import aoti_compile_and_package, aoti_load_package
-
-with TemporaryDirectory() as tmpdir:
-    path = str(Path(tmpdir) / "model.pt2")
-    with torch.no_grad():
-        pkg_path = aoti_compile_and_package(
-            exported_policy,
-            args=(),
-            kwargs={"pixels": pixels},
-            # Specify the generated shared library path
-            package_path=path,
-        )
-    print("pkg_path", pkg_path)
-
-    compiled_module = aoti_load_package(pkg_path)
-
-print(compiled_module(pixels=pixels))
-
-#####################################
-# An extra feature of AOTInductor is its capacity of dealing with dynamic shapes. This can be useful if you don't know
-# the shape of your input data ahead of time. For instance, we may want to run our policy for one, two or more
-# observations at a time. For this, let us re-export our policy, marking a new unsqueezed batch dimension as dynamic:
-
-batch_dim = torch.export.Dim("batch", min=1, max=32)
-pixels_unsqueeze = pixels.unsqueeze(0)
-exported_dynamic_policy = torch.export.export(
-    policy_transform,
-    args=(),
-    kwargs={"pixels": pixels_unsqueeze},
-    strict=False,
-    dynamic_shapes={"pixels": {0: batch_dim}},
-)
-# Then recompile and export
-pkg_path = aoti_compile_and_package(
-    exported_dynamic_policy,
-    args=(),
-    kwargs={"pixels": pixels_unsqueeze},
-    package_path=path,
-)
+# from tempfile import TemporaryDirectory
+#
+# from torch._inductor import aoti_compile_and_package, aoti_load_package
+#
+# with TemporaryDirectory() as tmpdir:
+#     path = str(Path(tmpdir) / "model.pt2")
+#     with torch.no_grad():
+#         pkg_path = aoti_compile_and_package(
+#             exported_policy,
+#             args=(),
+#             kwargs={"pixels": pixels},
+#             # Specify the generated shared library path
+#             package_path=path,
+#         )
+#     print("pkg_path", pkg_path)
+#
+#     compiled_module = aoti_load_package(pkg_path)
+#
+# print(compiled_module(pixels=pixels))
 
 #####################################
-# More information about this can be found in the
-# `AOTInductor tutorial <https://pytorch.org/tutorials/recipes/torch_export_aoti_python.html>`_.
 #
 # Exporting TorchRL models with ONNX
 # ----------------------------------