class_docstr: Assigns the `_value()` method to a TorchPolicy.
  
  This way, Policy can call `_value()` to get the current VF estimate on a
  single(!) observation (as done in `postprocess_trajectory_fn`).
  Note: When doing this, an actual forward pass is being performed.
  This is different from only calling `model.value_function()`, where
  the result of the most recent forward pass is being used to return an
  already calculated tensor.
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docstr: Defines extra fetches per action computation.
  
  Args:
      input_dict (Dict[str, TensorType]): The input dict used for the action
          computing forward pass.
      state_batches (List[TensorType]): List of state tensors (empty for
          non-RNNs).
      model (ModelV2): The Model object of the Policy.
      action_dist: The instantiated distribution
          object, resulting from the model's outputs and the given
          distribution class.
  
  Returns:
      Dict[str, TensorType]: Dict with extra tf fetches to perform per
          action computation.
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code: def extra_action_out(
      self, input_dict, state_batches, model, action_dist
  ):
      """Defines extra fetches per action computation.
  
      Args:
          input_dict (Dict[str, TensorType]): The input dict used for the action
              computing forward pass.
          state_batches (List[TensorType]): List of state tensors (empty for
              non-RNNs).
          model (ModelV2): The Model object of the Policy.
          action_dist: The instantiated distribution
              object, resulting from the model's outputs and the given
              distribution class.
  
      Returns:
          Dict[str, TensorType]: Dict with extra tf fetches to perform per
              action computation.
      """
      # Return value function outputs. VF estimates will hence be added to
      # the SampleBatches produced by the sampler(s) to generate the train
      # batches going into the loss function.
      return {
          SampleBatch.VF_PREDS: model.value_function(),
      }
  
imports: from ray.rllib.policy.policy import Policy, PolicyState
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_policy import TorchPolicy
  from ray.rllib.utils.annotations import DeveloperAPI, override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.schedules import PiecewiseSchedule
label_desc: Use image node_id 2 for calling the ValueNetworkMixin obj's underlying member method code with example usage: obj.extra_action_out(input_dict, state_batches, model, action_dist) and returns: dict