code: def normalize_gradient_l2_norms(grad: ep.Tensor) -> ep.Tensor:
      norms = ep.norms.l2(flatten(grad), -1)
  
      # remove zero gradients
      grad = ep.where(
          atleast_kd(norms == 0, grad.ndim),
          ep.normal(grad, shape=grad.shape),
          grad,
      )
      # calculate norms again for previously vanishing elements
      norms = ep.norms.l2(flatten(grad), -1)
  
      norms = ep.maximum(norms, 1e-12)  # avoid division by zero
      factor = 1 / norms
      factor = atleast_kd(factor, grad.ndim)
      return grad * factor
  
file_path: foolbox/foolbox/attacks/ddn.py
imports: from typing import Union, Tuple, Optional, Any
  import math
  import eagerpy
  from ..models import Model
  from ..criteria import Misclassification, TargetedMisclassification
  from ..distances import l2
  from ..devutils import atleast_kd, flatten
  from .base import MinimizationAttack
  from .base import get_criterion
  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 a global function with example usage: normalize_gradient_l2_norms(grad) and returns: unknown