code: def multi_perspective_match_pairwise(
      vector1: torch.Tensor, vector2: torch.Tensor, weight: torch.Tensor
  ) -> torch.Tensor:
      """
      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)`
  
      # Returns
  
      `torch.Tensor` :
          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=tiny_value_of_dtype(norm_value.dtype))
      ).permute(0, 2, 3, 1)
  
file_path: allennlp/allennlp/modules/bimpm_matching.py
imports: from typing import Tuple, List
  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, tiny_value_of_dtype
label_desc: Use image node_id 2 for calling a global function with example usage: multi_perspective_match_pairwise(vector1, vector2, weight) and returns: unknown