code: def squared_pairwise_distance(
      x: torch.Tensor, y: torch.Tensor, a_min: float = 1e-30
  ) -> torch.Tensor:
      """
      PyTorch pairwise squared Euclidean distance between samples x and y.
  
      Parameters
      ----------
      x
          Batch of instances of shape [Nx, features].
      y
          Batch of instances of shape [Ny, features].
      a_min
          Lower bound to clip distance values.
      Returns
      -------
      Pairwise squared Euclidean distance [Nx, Ny].
      """
      x2 = x.pow(2).sum(dim=-1, keepdim=True)
      y2 = y.pow(2).sum(dim=-1, keepdim=True)
      dist = torch.addmm(
          y2.transpose(-2, -1), x, y.transpose(-2, -1), alpha=-2
      ).add_(x2)
      return dist.clamp_min_(a_min)
  
file_path: alibi-detect/alibi_detect/utils/pytorch/distance.py
imports: import logging
  import torch
  from torch import nn
  import numpy
  from typing import Callable, List, Tuple, Optional, Union
label_desc: Use image node_id 1 for calling a global function with example usage: squared_pairwise_distance(x, y, a_min) and returns: dist