code: def __init__(
      self,
      pretrained_word_emb_name="840B",
      pretrained_word_emb_url=None,
      pretrained_word_emb_cache_dir=".vector_cache/",
      pretrained_word_emb_dim=300,
  ):
      if (
          pretrained_word_emb_name in GloVe.url.keys()
          and pretrained_word_emb_url is None
      ):
          url = GloVe.url[pretrained_word_emb_name]
          name = "glove.{}.{}d.txt".format(
              pretrained_word_emb_name, str(pretrained_word_emb_dim)
          )
      else:
          url = pretrained_word_emb_url
          name = pretrained_word_emb_name
      super().__init__(
          name=name, cache=pretrained_word_emb_cache_dir, url=url
      )
  
imports: import os
  import pickle
  import re
  import warnings
  from collections import Counter
  from functools import lru_cache
  import numpy
  import torch
  from nltk.tokenize import word_tokenize
  from torchtext.vocab import GloVe, Vectors
  from .None import constants
label_desc: Use image node_id 1 to create a new WordEmbModel object from inherited base classes: Vectors with example: obj = WordEmbModel(pretrained_word_emb_name, pretrained_word_emb_url, pretrained_word_emb_cache_dir, pretrained_word_emb_dim)