class_docstr: Minimization attacks try to find adversarials with minimal perturbation sizes
class_docstr_tok: ["Minimization","attacks","try","to","find","adversarials","with","minimal","perturbation","sizes"]
code: def __call__(
      self,
      model: Model,
      inputs: T,
      criterion: Any,
      *,
      epsilons: Union[Sequence[Union[float, None]], float, None],
      **kwargs: Any,
  ) -> Union[Tuple[List[T], List[T], T], Tuple[T, T, T]]:
      x, restore_type = ep.astensor_(inputs)
      del inputs
  
      verify_input_bounds(x, model)
  
      criterion = get_criterion(criterion)
      is_adversarial = get_is_adversarial(criterion, model)
  
      was_iterable = True
      if not isinstance(epsilons, Iterable):
          epsilons = [epsilons]
          was_iterable = False
  
      N = len(x)
      K = len(epsilons)
  
      # None means: just minimize, no early stopping, no limit on the perturbation size
      if any(eps is None for eps in epsilons):
          early_stop = None
      else:
          early_stop = min(epsilons)  # type: ignore
  
      # run the actual attack
      xp = self.run(
          model, x, criterion, early_stop=early_stop, **kwargs
      )
  
      xpcs = []
      success = []
      for epsilon in epsilons:
          if epsilon is None:
              xpc = xp
          else:
              xpc = self.distance.clip_perturbation(x, xp, epsilon)
          is_adv = is_adversarial(xpc)
  
          xpcs.append(xpc)
          success.append(is_adv)
  
      success_ = ep.stack(success)
      assert success_.shape == (K, N)
  
      xp_ = restore_type(xp)
      xpcs_ = [restore_type(xpc) for xpc in xpcs]
  
      if was_iterable:
          return [xp_] * K, xpcs_, restore_type(success_)
      else:
          assert len(xpcs_) == 1
          return xp_, xpcs_[0], restore_type(success_.squeeze(axis=0))
  
imports: from typing import Callable, TypeVar, Any, Union, Optional, Sequence, List, Tuple, Dict
  from typing_extensions import final, overload
  from abc import ABC, abstractmethod
  from collections.abc import Iterable
  import eagerpy
  from ..models import Model
  from ..criteria import Criterion
  from ..criteria import Misclassification
  from ..devutils import atleast_kd
  from ..distances import Distance
label_desc: Use image node_id 4 for calling the MinimizationAttack obj's underlying member method code with example usage: obj.__call__(model, inputs, criterion) and returns: unknown,  xpcs_,  restore_type, xp_,  xpcs_,  restore_type