code: def _compute_loss_packedsequences(
      criterion: nn.L1Loss, x: PackedSequence
  ) -> torch.Tensor:
      r"""
      This function computes the loss in a different way for 'mean' reduced L1 loss while for 'sum' reduced L1 loss,
      it computes the same way as with non-packed data. For 'mean' reduced L1 loss, it transforms x (PackedSequence)
      into a list of tensors such that this list of tensors was used to create this PackedSequence in the first
      place using batch_first=True and then takes the mean of the loss values produced from applying criterion on
      each sequence sample.
  
      Args:
          criterion: An L1 loss function with reduction either set to 'sum' or 'mean'.
          x: Data in the form of a PackedSequence.
  
      Returns:
          A loss variable, reduced either using summation or averaging from L1 errors.
      """
  
      if criterion.reduction == "sum":
          y = torch.zeros_like(x[0])
          return criterion(x[0], y)
      elif criterion.reduction == "mean":
          x = unpack_packedsequences(x)
          loss_sum = 0
          for x_i in x:
              y_i = torch.zeros_like(x_i)
              loss_sum += criterion(x_i, y_i)
          loss_mean = loss_sum / len(x)
          return loss_mean
  
imports: import io
  from typing import Callable, Dict, Iterable, List, Union
  import numpy
  import torch
  import torch.nn
  from opacus.grad_sample import wrap_model
  from opacus.utils.module_utils import trainable_parameters
  from opacus.utils.packed_sequences import compute_seq_lengths
  from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence
label_desc: Use image node_id 12 for calling a global function with example usage: _compute_loss_packedsequences(criterion, x) and returns: criterion, loss_mean