code: def _check_per_sample_gradients_are_correct_with_reduction(
      x: Union[torch.Tensor, PackedSequence],
      module: nn.Module,
      batch_first: bool = True,
      loss_reduction: str = "mean",
      atol: float = 10e-6,
      rtol: float = 10e-5,
      grad_sample_mode: str = "hooks",
  ) -> bool:
      (
          microbatch_grad_samples,
          opacus_grad_samples,
      ) = compute_grad_samples_microbatch_and_opacus(
          x,
          module,
          batch_first=batch_first,
          loss_reduction=loss_reduction,
          grad_sample_mode=grad_sample_mode,
      )
  
      for name, opacus_grad_sample in opacus_grad_samples.items():
          microbatch_grad_sample = microbatch_grad_samples[name]
          if (
              not opacus_grad_sample.shape
              == microbatch_grad_sample.shape
          ):
              return False
          if not torch.allclose(
              microbatch_grad_sample, opacus_grad_sample, atol, rtol
          ):
              return False
      return True
  
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 10 for calling a global function with example usage: _check_per_sample_gradients_are_correct_with_reduction(x, module, batch_first, loss_reduction, atol, rtol, grad_sample_mode) and returns: True, False, False