code: def norm(x: np.ndarray, p: int) -> np.ndarray:
      """
      Compute p-norm across the features of a batch of instances.
  
      Parameters
      ----------
      x
          Batch of instances of shape [N, features].
      p
          Power of the norm.
  
      Returns
      -------
      Array where p-norm is applied to the features.
      """
      return (x**p).sum(axis=1) ** (1 / p)
  
file_path: alibi-detect/alibi_detect/utils/distance.py
imports: import numpy
  from sklearn.manifold import MDS
  from typing import Any, Dict, Tuple
label_desc: Use image node_id 1 for calling a global function with example usage: norm(x, p) and returns: unknown