class_docstr: Transformation that fuses the bias to the weight in torch.nn.Linear.
class_docstr_tok: ["Transformation","that","fuses","the","bias","to","the","weight","in","torch.nn.Linear","."]
code: def reverse(self, graph_module: "GraphModule") -> "GraphModule":
      for node in self.get_transformed_nodes(graph_module):
          node.args = (node.start_node,)
          n = node.end_node
          while n is not node.start_node:
              if n not in node.nodes_to_ignore:
                  graph_module.graph.erase_node(n)
              n = n.prev
          self.mark_as_restored(node)
          module = graph_module.get_submodule(node.target)
          new_weight = torch.nn.Parameter(module.weight[:, :-1])
          new_bias = torch.nn.Parameter(module.weight[:, -1].squeeze())
          module.weight = new_weight
          module.bias = new_bias
      return graph_module
  
imports: import collections
  import copy
  import functools
  import itertools
  import operator
  import warnings
  from abc import ABC, abstractmethod
  from typing import TYPE_CHECKING, List
  import torch
  from transformers.file_utils import add_end_docstrings
  from transformers.utils.fx import _gen_constructor_wrapper
label_desc: Use image node_id 2 for calling the FuseBiasInLinear obj's underlying member method code with example usage: obj.reverse(graph_module) and returns: graph_module