code: def compute_opacus_grad_sample(
      x: Union[torch.Tensor, PackedSequence],
      module: nn.Module,
      batch_first: bool = True,
      loss_reduction: str = "mean",
      grad_sample_mode: str = "hooks",
  ) -> Dict[str, torch.tensor]:
      """
      Runs Opacus to compute per-sample gradients and return them for testing purposes.
  
      Args:
          x: Sample input batch
          module: The nn.Module you want to test.
          batch_first: Whether batch size is the first dimension (as opposed to the second).
              Defaults to True.
          loss_reduction: What reduction to apply to the loss. Defaults to "mean".
          grad_sample_mode: What sampling method to use to get gradients.
  
      Returns:
          Dictionary mapping parameter_name -> per-sample-gradient for that parameter
      """
      torch.use_deterministic_algorithms(True)
      torch.manual_seed(0)
      np.random.seed(0)
  
      gs_module = wrap_model(
          model=clone_module(module),
          grad_sample_mode=grad_sample_mode,
          batch_first=batch_first,
          loss_reduction=loss_reduction,
      )
      grad_sample_module = ModelWithLoss(gs_module, loss_reduction)
  
      grad_sample_module.zero_grad()
      loss = grad_sample_module(x)
      loss.backward()
  
      opacus_grad_samples = {
          name: p.grad_sample
          for name, p in trainable_parameters(
              grad_sample_module.wrapped_module._module
          )
      }
  
      return 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 4 for calling a global function with example usage: compute_opacus_grad_sample(x, module, batch_first, loss_reduction, grad_sample_mode) and returns: opacus_grad_samples