code: def _update(self, batch: NestedDict) -> Tuple[Any, Any, Any]:
      # The first time we call _update after building the learner or
      # adding/removing models, we update with the uncompiled update method.
      # This makes it so that any variables that may be created during the first
      # update step are already there when compiling. More specifically,
      # this avoids errors that occur around using defaultdicts with
      # torch.compile().
      if (
          self._torch_compile_complete_update
          and not self._compiled_update_initialized
      ):
          self._compiled_update_initialized = True
          return self._uncompiled_update(batch)
      else:
          return self._possibly_compiled_update(batch)
  
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 17 for calling the TorchLearner obj's underlying member method code with example usage: obj._update(batch) and returns: self, self