code: def compute_microbatch_grad_sample_tensor_or_seq(
      x: Union[torch.Tensor, PackedSequence],
      module: nn.Module,
      batch_first: bool = True,
      loss_reduction: str = "mean",
  ):
      if type(x) is PackedSequence:
          x_unpacked = unpack_packedsequences(x)
          microbatch_grad_samples = compute_microbatch_grad_sample(
              x_unpacked,
              module,
              batch_first=batch_first,
              loss_reduction=loss_reduction,
          )
      else:
          microbatch_grad_samples = compute_microbatch_grad_sample(
              x,
              module,
              batch_first=batch_first,
              loss_reduction=loss_reduction,
          )
  
      return microbatch_grad_samples
  
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 8 for calling a global function with example usage: compute_microbatch_grad_sample_tensor_or_seq(x, module, batch_first, loss_reduction) and returns: microbatch_grad_samples