code: def __init__(self, **kwargs):
      # by default in rllib we disable tf2 behavior
      # This call re-enables it as it is needed for using
      # this class.
      try:
          tf1.enable_v2_behavior()
      except ValueError:
          # This is a hack to avoid the error that happens when calling
          # enable_v2_behavior after variables have already been created.
          pass
  
      super().__init__(**kwargs)
  
      self._enable_tf_function = self.config.eager_tracing
  
      # This is a placeholder which will be filled by
      # `_make_distributed_strategy_if_necessary`.
      self._strategy: tf.distribute.Strategy = None
  
imports: import json
  import logging
  import pathlib
  from typing import Any, Callable, Dict, Hashable, Sequence, Tuple, TYPE_CHECKING, Union
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.rl_module import RLModule, SingleAgentRLModuleSpec
  from ray.rllib.core.rl_module.tf.tf_rl_module import TfRLModule
  from ray.rllib.policy.eager_tf_policy import _convert_to_tf
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils.annotations import override, OverrideToImplementCustomLogic
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.metrics import ALL_MODULES
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.serialization import convert_numpy_to_python_primitives
  from ray.rllib.utils.typing import ModuleID, Optimizer, Param, ParamDict, TensorType
label_desc: Use image node_id 1 to create a new TfLearner object from inherited base classes: Learner with example: obj = TfLearner()