code: def reduce(self, losses, target_lengths):
      if isinstance(losses, List):
          losses = torch.cat(losses, 0)
          target_lengths = torch.cat(target_lengths, 0)
  
      if self.reduction == "mean_batch":
          losses = losses.mean()  # global batch size average
      elif self.reduction == "mean":
          losses = torch.div(losses, target_lengths).mean()
      elif self.reduction == "sum":
          losses = losses.sum()
      elif self.reduction == "mean_volume":
          losses = (
              losses.sum() / target_lengths.sum()
          )  # same as above but longer samples weigh more
  
      return losses
  
imports: import inspect
  import operator
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Set
  import torch
  from omegaconf import DictConfig, OmegaConf
  from nemo.collections.asr.losses.rnnt_pytorch import MultiblankRNNTLossPytorch, RNNTLossPytorch, TDTLossPytorch
  from nemo.core.classes import Loss, typecheck
  from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, LossType, NeuralType
  from nemo.core.utils import numba_utils
  from nemo.core.utils.k2_utils import K2_INSTALLATION_MESSAGE
  from nemo.core.utils.numba_utils import NUMBA_INSTALLATION_MESSAGE
  from nemo.utils import logging, logging_mode, model_utils
label_desc: Use image node_id 4 for calling the RNNTLoss obj's underlying member method code with example usage: obj.reduce(losses, target_lengths) and returns: losses