code: def value_and_grad(
      # can be overridden by users
      self,
      loss_fn: Callable[[ep.Tensor], ep.Tensor],
      x: ep.Tensor,
  ) -> Tuple[ep.Tensor, ep.Tensor]:
      return ep.value_and_grad(loss_fn, 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 4 for calling the BaseGradientDescent obj's underlying member method code with example usage: obj.value_and_grad(loss_fn, x) and returns: ep