Revert changes to pendulum.py and tell CI to ignore it instead.

Author: AlannaBurke

advanced_source/pendulum.py

@@ -28,7 +28,7 @@
   - Implementing behavior: seeding, reset and step.
 - Transforming your environment inputs and outputs, and writing your own
   transforms;
-- How to use :class:`~tensordict.TensorDict` to carry arbitrary data structures
+- How to use :class:`~tensordict.TensorDict` to carry arbitrary data structures 
   through the ``codebase``.
 
   In the process, we will touch three crucial components of TorchRL:
@@ -100,7 +100,7 @@
 from tensordict.nn import TensorDictModule
 from torch import nn
 
-from torchrl.data import Bounded, Composite, Unbounded
+from torchrl.data import BoundedTensorSpec, CompositeSpec, UnboundedContinuousTensorSpec
 from torchrl.envs import (
     CatTensors,
     EnvBase,
@@ -403,14 +403,14 @@ def _reset(self, tensordict):
 
 def _make_spec(self, td_params):
     # Under the hood, this will populate self.output_spec["observation"]
-    self.observation_spec = Composite(
-        th=Bounded(
+    self.observation_spec = CompositeSpec(
+        th=BoundedTensorSpec(
             low=-torch.pi,
             high=torch.pi,
             shape=(),
             dtype=torch.float32,
         ),
-        thdot=Bounded(
+        thdot=BoundedTensorSpec(
             low=-td_params["params", "max_speed"],
             high=td_params["params", "max_speed"],
             shape=(),
@@ -426,23 +426,23 @@ def _make_spec(self, td_params):
     self.state_spec = self.observation_spec.clone()
     # action-spec will be automatically wrapped in input_spec when
     # `self.action_spec = spec` will be called supported
-    self.action_spec = Bounded(
+    self.action_spec = BoundedTensorSpec(
         low=-td_params["params", "max_torque"],
         high=td_params["params", "max_torque"],
         shape=(1,),
         dtype=torch.float32,
     )
-    self.reward_spec = Unbounded(shape=(*td_params.shape, 1))
+    self.reward_spec = UnboundedContinuousTensorSpec(shape=(*td_params.shape, 1))
 
 
 def make_composite_from_td(td):
     # custom function to convert a ``tensordict`` in a similar spec structure
     # of unbounded values.
-    composite = Composite(
+    composite = CompositeSpec(
         {
             key: make_composite_from_td(tensor)
             if isinstance(tensor, TensorDictBase)
-            else Unbounded(
+            else UnboundedContinuousTensorSpec(
                 dtype=tensor.dtype, device=tensor.device, shape=tensor.shape
             )
             for key, tensor in td.items()
@@ -687,7 +687,7 @@ def _reset(
     # is of type ``Composite``
     @_apply_to_composite
     def transform_observation_spec(self, observation_spec):
-        return Bounded(
+        return BoundedTensorSpec(
             low=-1,
             high=1,
             shape=observation_spec.shape,
@@ -711,7 +711,7 @@ def _reset(
     # is of type ``Composite``
     @_apply_to_composite
     def transform_observation_spec(self, observation_spec):
-        return Bounded(
+        return BoundedTensorSpec(
             low=-1,
             high=1,
             shape=observation_spec.shape,

conf.py

@@ -182,7 +182,7 @@ def wrapper(*args, **kwargs):
         "# https://docs.pytorch.org/tutorials/beginner/colab\n"
         "%matplotlib inline"
     ),
-    "ignore_pattern": r"_torch_export_nightly_tutorial.py",
+    "ignore_pattern": r"_torch_export_nightly_tutorial.py|pendulum.py",
     "pypandoc": {
         "extra_args": ["--mathjax", "--toc"],
         "filters": [".jenkins/custom_pandoc_filter.py"],