code: def uniform_l2_n_spheres(
      dummy: ep.Tensor, batch_size: int, n: int
  ) -> ep.Tensor:
      x = ep.normal(dummy, (batch_size, n + 1))
      r = x.norms.l2(axis=-1, keepdims=True)
      s = x / r
      return s
  
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 a global function with example usage: uniform_l2_n_spheres(dummy, batch_size, n) and returns: s