docstr: Create a distributed strategy for the learner.
  
  A stratgey is a tensorflow object that is used for distributing training and
  gradient computation across multiple devices. By default a no-op strategy is
  used that is not distributed.
  
  Returns:
      A strategy for the learner to use for distributed training.
docstr_tok: ["Create","a","distributed","strategy","for","the","learner",".","A","stratgey","is","a","tensorflow","object","that","is","used","for","distributing","training","and","gradient","computation","across","multiple","devices",".","By","default","a","no-op","strategy","is","used","that","is","not","distributed",".","Returns",":","A","strategy","for","the","learner","to","use","for","distributed","training","."]
code: def _make_distributed_strategy_if_necessary(
      self,
  ) -> "tf.distribute.Strategy":
      """Create a distributed strategy for the learner.
  
      A stratgey is a tensorflow object that is used for distributing training and
      gradient computation across multiple devices. By default a no-op strategy is
      used that is not distributed.
  
      Returns:
          A strategy for the learner to use for distributed training.
  
      """
      if self._distributed:
          strategy = tf.distribute.MultiWorkerMirroredStrategy()
      elif self._use_gpu:
          # mirrored strategy is typically used for multi-gpu training
          # on a single machine, however we can use it for single-gpu
          devices = tf.config.list_logical_devices("GPU")
          assert self._local_gpu_idx < len(devices), (
              f"local_gpu_idx {self._local_gpu_idx} is not a valid GPU id or is "
              "not available."
          )
          local_gpu = [devices[self._local_gpu_idx].name]
          strategy = tf.distribute.MirroredStrategy(devices=local_gpu)
      else:
          # the default strategy is a no-op that can be used in the local mode
          # cpu only case, build will override this if needed.
          strategy = tf.distribute.get_strategy()
      return strategy
  
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 22 for calling the TfLearner obj's underlying member method code with example usage: obj._make_distributed_strategy_if_necessary() and returns: strategy