class_docstr: Base class for RLlib TensorFlow RLModules.
class_docstr_tok: ["Base","class","for","RLlib","TensorFlow","RLModules","."]
docstr: Forward pass of the module.
  
  Note:
      This is aliased to forward_train to follow the Keras Model API.
  
  Args:
      batch: The input batch. This input batch should comply with
          input_specs_train().
      **kwargs: Additional keyword arguments.
  
  Returns:
      The output of the forward pass. This output should comply with the
      ouptut_specs_train().
docstr_tok: ["Forward","pass","of","the","module",".","Note",":","This","is","aliased","to","forward_train","to","follow","the","Keras","Model","API",".","Args",":","batch",":","The","input","batch",".","This","input","batch","should","comply","with","input_specs_train","(",")",".","*","*","kwargs",":","Additional","keyword","arguments",".","Returns",":","The","output","of","the","forward","pass",".","This","output","should","comply","with","the","ouptut_specs_train","(",")","."]
code: def call(
      self, batch: Mapping[str, Any], **kwargs
  ) -> Mapping[str, Any]:
      """Forward pass of the module.
  
      Note:
          This is aliased to forward_train to follow the Keras Model API.
  
      Args:
          batch: The input batch. This input batch should comply with
              input_specs_train().
          **kwargs: Additional keyword arguments.
  
      Returns:
          The output of the forward pass. This output should comply with the
          ouptut_specs_train().
  
      """
      return self.forward_train(batch)
  
file_path: ray/rllib/core/rl_module/tf/tf_rl_module.py
imports: import pathlib
  from typing import Any, Mapping, Union
  from ray.rllib.core.rl_module import RLModule
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 2 for calling the TfRLModule obj's underlying member method code with example usage: obj.call(batch) and returns: self