code: def multi_perspective_match_pairwise(
      vector1, vector2, weight, eps=1e-8
  ):
      """
      Calculate multi-perspective cosine matching between each time step of
      one vector and each time step of another vector.
  
      Parameters
      ----------
      vector1 : ``torch.Tensor``
          A tensor of shape ``(batch, seq_len1, hidden_size)``
      vector2 : ``torch.Tensor``
          A tensor of shape ``(batch, seq_len2, hidden_size)``
      weight : ``torch.Tensor``
          A tensor of shape ``(num_perspectives, hidden_size)``
      eps : ``float`` optional, (default = 1e-8)
          A small value to avoid zero division problem
  
      Returns
      -------
      A tensor of shape (batch, seq_len1, seq_len2, num_perspectives) consisting
      multi-perspective matching results
      """
      num_perspectives = weight.size(0)
  
      # (1, num_perspectives, 1, hidden_size)
      weight = weight.unsqueeze(0).unsqueeze(2)
  
      # (batch, num_perspectives, seq_len*, hidden_size)
      vector1 = weight * vector1.unsqueeze(1).expand(
          -1, num_perspectives, -1, -1
      )
      vector2 = weight * vector2.unsqueeze(1).expand(
          -1, num_perspectives, -1, -1
      )
  
      # (batch, num_perspectives, seq_len*, 1)
      vector1_norm = vector1.norm(p=2, dim=3, keepdim=True)
      vector2_norm = vector2.norm(p=2, dim=3, keepdim=True)
  
      # (batch, num_perspectives, seq_len1, seq_len2)
      mul_result = torch.matmul(vector1, vector2.transpose(2, 3))
      norm_value = vector1_norm * vector2_norm.transpose(2, 3)
  
      # (batch, seq_len1, seq_len2, num_perspectives)
      return (mul_result / norm_value.clamp(min=eps)).permute(
          0, 2, 3, 1
      )
  
file_path: magnitude/pymagnitude/third_party/allennlp/modules/bimpm_matching.py
imports: from __future__ import division
  from __future__ import absolute_import
  import torch
  import torch.nn
  import torch.nn.functional
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.registrable import FromParams
  from allennlp.nn.util import get_lengths_from_binary_sequence_mask, masked_max, masked_mean, masked_softmax
label_desc: Use image node_id 2 for calling a global function with example usage: multi_perspective_match_pairwise(vector1, vector2, weight, eps) and returns: unknown