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: Returns a hash that can be used to identify the transformation.
docstr_tok: ["Returns","a","hash","that","can","be","used","to","identify","the","transformation","."]
code: def signature(self):
      """
      Returns a hash that can be used to identify the transformation.
      """
      attributes_to_use_for_hashing = vars(self)
      attributes_to_use_for_hashing[""] = self.__class__
      hash_str = "_".join(
          f"{k}_{hash(v)}"
          for k, v in attributes_to_use_for_hashing.items()
      )
      return hash(hash_str)
  
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 3 for calling the Transformation obj's underlying member method code with example usage: obj.signature() and returns: hash