class_docstr: ExpandedWeights-based implementation of AbstractGradSampleModule
  
  Computes per-sample gradients using PyTorch built-in mechanism of ExpandedWeights.
  See README.md for more details
code: def forward(self, x: torch.Tensor, *args, **kwargs):
      from torch.nn.utils._per_sample_grad import (
          call_for_per_sample_grads,
      )
  
      return call_for_per_sample_grads(
          module=self._module,
          batch_size=x.shape[0],
          loss_reduction=self.loss_reduction,
      )(x, *args, **kwargs)
  
imports: import torch
  import torch.nn
  from opacus.grad_sample.gsm_base import AbstractGradSampleModule
label_desc: Use image node_id 2 for calling the GradSampleModuleExpandedWeights obj's underlying member method code with example usage: obj.forward(x) and returns: call_for_per_sample_grads