code: def uniform_l2_n_balls(
      dummy: ep.Tensor, batch_size: int, n: int
  ) -> ep.Tensor:
      """Sampling from the n-ball
  
      Implementation of the algorithm proposed by Voelker et al. [#Voel17]_
  
      References:
          .. [#Voel17] Voelker et al., 2017, Efficiently sampling vectors and coordinates
              from the n-sphere and n-ball
              http://compneuro.uwaterloo.ca/files/publications/voelker.2017.pdf
      """
      s = uniform_l2_n_spheres(dummy, batch_size, n + 1)
      b = s[:, :n]
      return b
  
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 a global function with example usage: uniform_l2_n_balls(dummy, batch_size, n) and returns: b