code: def concat_multi_gpu_td_errors(
      policy: Union["TorchPolicy", "TorchPolicyV2"]
  ) -> Dict[str, TensorType]:
      """Concatenates multi-GPU (per-tower) TD error tensors given TorchPolicy.
  
      TD-errors are extracted from the TorchPolicy via its tower_stats property.
  
      Args:
          policy: The TorchPolicy to extract the TD-error values from.
  
      Returns:
          A dict mapping strings "td_error" and "mean_td_error" to the
          corresponding concatenated and mean-reduced values.
      """
      td_error = torch.cat(
          [
              t.tower_stats.get("td_error", torch.tensor([0.0])).to(
                  policy.device
              )
              for t in policy.model_gpu_towers
          ],
          dim=0,
      )
      policy.td_error = td_error
      return {
          "td_error": td_error,
          "mean_td_error": torch.mean(td_error),
      }
  
imports: import logging
  import os
  import warnings
  from typing import Dict, List, Optional, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from packaging import version
  import ray
  from ray.rllib.models.repeated_values import RepeatedValues
  from ray.rllib.utils.annotations import Deprecated, PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import SMALL_NUMBER
  from ray.rllib.utils.typing import LocalOptimizer, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 4 for calling a global function with example usage: concat_multi_gpu_td_errors(policy) and returns: dict