class_docstr: A torch.fx graph transformation.
  
  It  must implement the [`~optimum.fx.optimization.ReversibleTransformation.transform`] method, and be used as a
  callable.
class_docstr_tok: ["A","torch.fx","graph","transformation",".","It","must","implement","the","[","`","~optimum.fx.optimization.ReversibleTransformation.transform","`","]","method",",","and","be","used","as","a","callable","."]
docstr: Args:
      graph_module (`torch.fx.GraphModule`):
          The graph_module to get the nodes from.
  
  Returns:
      `List[torch.fx.Node]`:
          Gives the list of nodes that were transformed by the transformation.
docstr_tok: ["Args",":","graph_module","(","`","torch.fx.GraphModule","`",")",":","The","graph_module","to","get","the","nodes","from",".","Returns",":","`","List","[","torch.fx.Node","]","`",":","Gives","the","list","of","nodes","that","were","transformed","by","the","transformation","."]
code: def get_transformed_nodes(
      self, graph_module: "GraphModule"
  ) -> List["Node"]:
      """
      Args:
          graph_module (`torch.fx.GraphModule`):
              The graph_module to get the nodes from.
  
      Returns:
          `List[torch.fx.Node]`:
              Gives the list of nodes that were transformed by the transformation.
      """
  
      return [
          node
          for node in graph_module.graph.nodes
          if self.transformed(node)
      ]
  
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 6 for calling the Transformation obj's underlying member method code with example usage: obj.get_transformed_nodes(graph_module) and returns: unknown