class_docstr: Creates "negative images" by inverting the pixel values. [#Hos16]_
  
  References:
      .. [#Hos16] Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, Radha Poovendran,
             "On the Limitation of Convolutional Neural Networks in Recognizing
             Negative Images",
             https://arxiv.org/abs/1607.02533
class_docstr_tok: ["Creates","``","negative","images","''","by","inverting","the","pixel","values",".","[","#","Hos16","]","_","References",":","..","[","#","Hos16","]","Hossein","Hosseini",",","Baicen","Xiao",",","Mayoore","Jaiswal",",","Radha","Poovendran",",","``","On","the","Limitation","of","Convolutional","Neural","Networks","in","Recognizing","Negative","Images","''",",","https",":","\/\/arxiv.org\/abs\/1607.02533"]
code: def run(
      self,
      model: Model,
      inputs: T,
      criterion: Union[Criterion, Any] = None,
      *,
      early_stop: Optional[float] = None,
      **kwargs: Any,
  ) -> T:
      raise_if_kwargs(kwargs)
      x, restore_type = ep.astensor_(inputs)
      del inputs, criterion, kwargs
  
      verify_input_bounds(x, model)
  
      min_, max_ = model.bounds
      x = min_ + max_ - x
      return restore_type(x)
  
file_path: foolbox/foolbox/attacks/inversion.py
imports: from typing import Union, Any, Optional
  import eagerpy
  from ..criteria import Criterion
  from ..models import Model
  from .base import FlexibleDistanceMinimizationAttack
  from .base import T
  from .base import raise_if_kwargs
  from .base import verify_input_bounds
label_desc: Use image node_id 1 for calling the InversionAttack obj's underlying member method code with example usage: obj.run(model, inputs, criterion) and returns: restore_type