code: def __init__(self, **kwargs):
      super().__init__(**kwargs)
  
      # Will be set during build.
      self._device = None
  
      # Whether to compile the RL Module of this learner. This implies that the.
      # forward_train method of the RL Module will be compiled. Further more,
      # other forward methods of the RL Module will be compiled on demand.
      # This is assumed to not happen, since other forwrad methods are not expected
      # to be used during training.
      self._torch_compile_forward_train = False
      self._torch_compile_cfg = None
      # Whether to compile the `_uncompiled_update` method of this learner. This
      # implies that everything within `_uncompiled_update` will be compiled,
      # not only the forward_train method of the RL Module.
      # Note that this is experimental.
      # Note that this requires recompiling the forward methods once we add/remove
      # RL Modules.
      self._torch_compile_complete_update = False
      if self.config.torch_compile_learner:
          if (
              self.config.torch_compile_learner_what_to_compile
              == TorchCompileWhatToCompile.COMPLETE_UPDATE
          ):
              self._torch_compile_complete_update = True
              self._compiled_update_initialized = False
          else:
              self._torch_compile_forward_train = True
  
          self._torch_compile_cfg = TorchCompileConfig(
              torch_dynamo_backend=self.config.torch_compile_learner_dynamo_backend,
              torch_dynamo_mode=self.config.torch_compile_learner_dynamo_mode,
          )
  
imports: import logging
  import pathlib
  from typing import Any, Callable, Dict, Hashable, Sequence, Union, Tuple
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, TorchCompileWhatToCompile
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule
  from ray.rllib.core.rl_module.rl_module import RLModule, SingleAgentRLModuleSpec
  from ray.rllib.core.rl_module.torch.torch_rl_module import TorchCompileConfig, TorchDDPRLModule, TorchRLModule
  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_torch
  from ray.rllib.utils.metrics import ALL_MODULES
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor, copy_torch_tensors
  from ray.rllib.utils.typing import ModuleID, Optimizer, Param, ParamDict, TensorType
label_desc: Use image node_id 1 to create a new TorchLearner object from inherited base classes: Learner with example: obj = TorchLearner()