class_docstr: Class which converts the supplied pytorch model into an equivalent model
  which uses abstract operations
docstr: Helper function to set the forward mode of the model
  
  :param mode: either concrete, abstract, or attack to signify how to run the forward pass
docstr_tok: ["Helper","function","to","set","the","forward","mode","of","the","model",":","param","mode",":","either","concrete",",","abstract",",","or","attack","to","signify","how","to","run","the","forward","pass"]
code: def set_forward_mode(self, mode: str) -> None:
      """
      Helper function to set the forward mode of the model
  
      :param mode: either concrete, abstract, or attack to signify how to run the forward pass
      """
      assert mode in {"concrete", "abstract", "attack"}
      self.forward_mode = mode
  
imports: from typing import List, Optional, Tuple, Union, Callable, Any, TYPE_CHECKING
  import logging
  import warnings
  import numpy
  import torch
  from art.estimators.certification.interval.interval import PyTorchIntervalConv2D, PyTorchIntervalDense, PyTorchIntervalReLU, PyTorchIntervalBounds
  from art.estimators.classification.pytorch import PyTorchClassifier
label_desc: Use image node_id 5 for calling the ConvertedModel obj's underlying member method code with example usage: obj.set_forward_mode(mode) without return types