class_docstr: Wraps Gensim's `models.keyedvectors` module
  (https://radimrehurek.com/gensim/models/keyedvectors.html)
class_docstr_tok: ["Wraps","Gensim","'s","`","models.keyedvectors","`","module","(","https",":","\/\/radimrehurek.com\/gensim\/models\/keyedvectors.html",")"]
docstr: Return MSE distance between vector for word `a` and vector for word
  `b`.
  
  Since this is a metric, `get_mse_dist(a,b)` and `get_mse_dist(b,a)` should return the same value.
  Args:
      a (Union[str|int]): Either word or integer presenting the id of the word
      b (Union[str|int]): Either word or integer presenting the id of the word
  Returns:
      distance (float): MSE (L2) distance
docstr_tok: ["Return","MSE","distance","between","vector","for","word","`","a","`","and","vector","for","word","`","b","`",".","Since","this","is","a","metric",",","`","get_mse_dist","(","a",",","b",")","`","and","`","get_mse_dist","(","b",",","a",")","`","should","return","the","same","value",".","Args",":","a","(","Union","[","str|int","]",")",":","Either","word","or","integer","presenting","the","id","of","the","word","b","(","Union","[","str|int","]",")",":","Either","word","or","integer","presenting","the","id","of","the","word","Returns",":","distance","(","float",")",":","MSE","(","L2",")","distance"]
code: def get_mse_dist(self, a, b):
      """Return MSE distance between vector for word `a` and vector for word
      `b`.
  
      Since this is a metric, `get_mse_dist(a,b)` and `get_mse_dist(b,a)` should return the same value.
      Args:
          a (Union[str|int]): Either word or integer presenting the id of the word
          b (Union[str|int]): Either word or integer presenting the id of the word
      Returns:
          distance (float): MSE (L2) distance
      """
      try:
          mse_dist = self._mse_dist_mat[a][b]
      except KeyError:
          e1 = self.keyed_vectors.get_normed_vectors()[a]
          e2 = self.keyed_vectors.get_normed_vectors()[b]
          e1 = torch.tensor(e1).to(utils.device)
          e2 = torch.tensor(e2).to(utils.device)
          mse_dist = torch.sum((e1 - e2) ** 2).item()
          self._mse_dist_mat[a][b] = mse_dist
      return mse_dist
  
imports: from abc import ABC, abstractmethod
  from collections import defaultdict
  import os
  import pickle
  import numpy
  import torch
  from textattack.shared import utils
label_desc: Use image node_id 5 for calling the GensimWordEmbedding obj's underlying member method code with example usage: obj.get_mse_dist(a, b) and returns: mse_dist