code: def _get_tensor_variable(
      self, value, dtype=None, trainable=False
  ) -> "torch.Tensor":
      return torch.tensor(
          value,
          requires_grad=trainable,
          device=self._device,
          dtype=(
              dtype
              or (
                  torch.float32
                  if isinstance(value, float)
                  else torch.int32
                  if isinstance(value, int)
                  else None
              )
          ),
      )
  
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 23 for calling the TorchLearner obj's underlying member method code with example usage: obj._get_tensor_variable(value, dtype, trainable) and returns: torch