code: def run(
      self,
      model: Model,
      inputs: T,
      criterion: Union[Misclassification, TargetedMisclassification, T],
      *,
      epsilon: float,
      **kwargs: Any,
  ) -> T:
      raise_if_kwargs(kwargs)
      x0, restore_type = ep.astensor_(inputs)
      criterion_ = get_criterion(criterion)
      del inputs, criterion, kwargs
  
      verify_input_bounds(x0, model)
  
      # perform a gradient ascent (targeted attack) or descent (untargeted attack)
      if isinstance(criterion_, Misclassification):
          gradient_step_sign = 1.0
          classes = criterion_.labels
      elif hasattr(criterion_, "target_classes"):
          gradient_step_sign = -1.0
          classes = criterion_.target_classes
      else:
          raise ValueError("unsupported criterion")
  
      loss_fn = self.get_loss_fn(model, classes)
  
      if self.abs_stepsize is None:
          stepsize = self.rel_stepsize * epsilon
      else:
          stepsize = self.abs_stepsize
  
      optimizer = self.get_optimizer(x0, stepsize)
  
      if self.random_start:
          x = self.get_random_start(x0, epsilon)
          x = ep.clip(x, *model.bounds)
      else:
          x = x0
  
      for _ in range(self.steps):
          _, gradients = self.value_and_grad(loss_fn, x)
          gradients = self.normalize(
              gradients, x=x, bounds=model.bounds
          )
          x = x + gradient_step_sign * optimizer(gradients)
          x = self.project(x, x0, epsilon)
          x = ep.clip(x, *model.bounds)
  
      return restore_type(x)
  
imports: from typing import Union, Any, Optional, Callable, Tuple
  from abc import ABC, abstractmethod
  import eagerpy
  from ..devutils import flatten
  from ..devutils import atleast_kd
  from ..types import Bounds
  from ..models.base import Model
  from ..criteria import Misclassification, TargetedMisclassification
  from ..distances import l1, l2, linf
  from .base import FixedEpsilonAttack
  from .base import T
  from .base import get_criterion
  from .base import raise_if_kwargs
  from .base import verify_input_bounds
label_desc: Use image node_id 5 for calling the BaseGradientDescent obj's underlying member method code with example usage: obj.run(model, inputs, criterion) and returns: restore_type