class_docstr: To test the gradients of a module, we need to have a loss.
  This module makes it easy to get a loss from any nn.Module, and automatically generates
  a target y vector for it in the forward (of all zeros of the correct size).
  This reduces boilerplate while testing.
code: def forward(self, x):
      if type(x) is tuple:
          x = self.wrapped_module(*x)
      else:
          x = self.wrapped_module(x)
      if type(x) is PackedSequence:
          loss = _compute_loss_packedsequences(self.criterion, x)
      else:
          y = torch.zeros_like(x)
          loss = self.criterion(x, y)
      return loss
  
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 2 for calling the ModelWithLoss obj's underlying member method code with example usage: obj.forward(x) and returns: loss