code: def squared_pairwise_distance(
      x: np.ndarray,
      y: np.ndarray,
      a_min: float = 1e-7,
      a_max: float = 1e30,
  ) -> np.ndarray:
      """
      `numpy` pairwise squared Euclidean distance between samples `x` and `y`.
  
      Parameters
      ----------
      x
          A batch of instances of shape `Nx x features`.
      y
          A batch of instances of shape `Ny x features`.
      a_min
          Lower bound to clip distance values.
      a_max
          Upper bound to clip distance values.
  
      Returns
      -------
      Pairwise squared Euclidean distance `Nx x Ny`.
      """
      x2 = np.sum(x**2, axis=-1, keepdims=True)
      y2 = np.sum(y**2, axis=-1, keepdims=True)
      dist = (
          x2
          + np.transpose(y2, (1, 0))
          - 2.0 * x @ np.transpose(y, (1, 0))
      )
      return np.clip(dist, a_min=a_min, a_max=a_max)
  
file_path: alibi/alibi/utils/distance.py
imports: import numpy
  from sklearn.manifold import MDS
  from typing import Dict, Tuple, Callable, Optional, Union
label_desc: Use image node_id 5 for calling a global function with example usage: squared_pairwise_distance(x, y, a_min, a_max) and returns: np