code: def check_per_sample_gradients_are_correct(
      x: Union[torch.Tensor, PackedSequence],
      module: nn.Module,
      *,
      batch_first: bool = True,
      atol: float = 10e-6,
      rtol: float = 10e-5,
      grad_sample_mode: str = "hooks",
  ) -> bool:
      """
      A utility to check whether per sample gradients are computed correctly with a particular model.
      The check is performed by comparing the result of the slow but reliable micro-batch method `compute_microbatch_grad_sample`
      with the result of optimized opacus method.
  
      Args:
          x: Sample input batch
          module: The ``ModelWithLoss`` that wraps the nn.Module you want to check.
          batch_first: Whether batch size is the first dimension (as opposed to the second).
              Defaults to True.
          atol: The relative tolerance parameter (torch.allclose).
          rtol: The absolute tolerance parameter (torch.allclose).
          grad_sample_mode: What sampling method to use to get gradients.
  
      Returns: True if per sample gradients were computed correctly. False otherwise.
  
      Example:
          >>> N, Z, W = 100, 10, 10
          >>> x_shape = [N, Z, W]
          >>> x = torch.randn(x_shape)
          >>> model = nn.Linear(W, W + 2)
          >>> assert check_per_sample_gradients_are_correct(
          ...            x,
          ...            model
          ...        ) # This will fail only if the opacus per sample gradients do not match the micro-batch gradients.
      """
      reductions = ["sum", "mean"]
      if grad_sample_mode == "ew":
          if not batch_first:
              raise RuntimeError("Batch should be first dimension.")
          if not check_torch_version_for_ew_sample():
              raise RuntimeError(
                  f"Unsupported torch version: {torch.__version__}."
              )
  
      for loss_reduction in reductions:
          if not _check_per_sample_gradients_are_correct_with_reduction(
              x,
              module,
              batch_first=batch_first,
              loss_reduction=loss_reduction,
              atol=atol,
              rtol=rtol,
              grad_sample_mode=grad_sample_mode,
          ):
              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 7 for calling a global function with example usage: check_per_sample_gradients_are_correct(x, module) and returns: True, False