class_docstr: Extends nn.Module so that its parameter tensors have an extra field called .grad_sample.
class_docstr_tok: ["Extends","nn.Module","so","that","its","parameter","tensors","have","an","extra","field","called",".grad_sample","."]
docstr: Returns the standard nn.Module wrapped by this, eliminating all traces
  of grad samples and hooks
  
  Returns:
      The wrapped module
docstr_tok: ["Returns","the","standard","nn.Module","wrapped","by","this",",","eliminating","all","traces","of","grad","samples","and","hooks","Returns",":","The","wrapped","module"]
code: def to_standard_module(self) -> nn.Module:
      """
      Returns the standard nn.Module wrapped by this, eliminating all traces
      of grad samples and hooks
  
      Returns:
          The wrapped module
      """
      self._close()
      return self._module
  
imports: import logging
  from abc import ABC, abstractmethod
  from typing import Optional
  import torch.nn
  from opacus.utils.module_utils import trainable_parameters
  from torch.utils.hooks import RemovableHandle
label_desc: Use image node_id 7 for calling the AbstractGradSampleModule obj's underlying member method code with example usage: obj.to_standard_module() and returns: self