code: def compute_grad_samples_microbatch_and_opacus(
      x: Union[torch.Tensor, PackedSequence],
      module: nn.Module,
      batch_first: bool = True,
      loss_reduction: str = "mean",
      grad_sample_mode: str = "hooks",
      chunk_method: Callable = iter,
  ):
      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,
              chunk_method=chunk_method,
          )
      elif not is_batch_empty(x):
          microbatch_grad_samples = compute_microbatch_grad_sample(
              x,
              module,
              batch_first=batch_first,
              loss_reduction=loss_reduction,
              chunk_method=chunk_method,
          )
      else:
          raise RuntimeError("x is expected to be non-empty.")
  
      opacus_grad_samples = compute_opacus_grad_sample(
          x,
          module,
          batch_first=batch_first,
          loss_reduction=loss_reduction,
          grad_sample_mode=grad_sample_mode,
      )
  
      if microbatch_grad_samples.keys() != opacus_grad_samples.keys():
          raise ValueError(
              "Keys not matching! "
              f"Keys only in microbatch: {microbatch_grad_samples.keys() - opacus_grad_samples.keys()}; "
              f"Keys only in Opacus: {opacus_grad_samples.keys() - microbatch_grad_samples.keys()}"
          )
  
      return microbatch_grad_samples, opacus_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 9 for calling a global function with example usage: compute_grad_samples_microbatch_and_opacus(x, module, batch_first, loss_reduction, grad_sample_mode, chunk_method) and returns: microbatch_grad_samples,  opacus_grad_samples