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 __init__(
      self,
      m: nn.Module,
      *,
      batch_first=True,
      loss_reduction="mean",
  ):
      if not batch_first:
          raise NotImplementedError
  
      super().__init__(
          m,
          batch_first=batch_first,
          loss_reduction=loss_reduction,
      )
  
imports: import torch
  import torch.nn
  from opacus.grad_sample.gsm_base import AbstractGradSampleModule
label_desc: Use image node_id 1 to create a new GradSampleModuleExpandedWeights object from inherited base classes: AbstractGradSampleModule with example: obj = GradSampleModuleExpandedWeights(m)