class_docstr: The tf-specific RLModule class for DreamerV3.
  
  Serves mainly as a thin-wrapper around the `DreamerModel` (a tf.keras.Model) class.
class_docstr_tok: ["The","tf-specific","RLModule","class","for","DreamerV3",".","Serves","mainly","as","a","thin-wrapper","around","the","`","DreamerModel","`","(","a","tf.keras.Model",")","class","."]
code: def _forward_train(self, batch: NestedDict):
      # Call the Dreamer-Model's forward_train method and return its outputs as-is.
      return self.dreamer_model.forward_train(
          observations=batch[SampleBatch.OBS],
          actions=batch[SampleBatch.ACTIONS],
          is_first=batch["is_first"],
      )
  
file_path: ray/rllib/algorithms/dreamerv3/tf/dreamerv3_tf_rl_module.py
imports: from typing import Any, Dict
  from ray.rllib.algorithms.dreamerv3.dreamerv3_rl_module import DreamerV3RLModule
  from ray.rllib.core.models.base import STATE_IN, STATE_OUT
  from ray.rllib.core.rl_module.rl_module import RLModule
  from ray.rllib.core.rl_module.tf.tf_rl_module import TfRLModule
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.nested_dict import NestedDict
label_desc: Use image node_id 3 for calling the DreamerV3TfRLModule obj's underlying member method code with example usage: obj._forward_train(batch) and returns: self