class_docstr: The GloVe word embedding.
  
  
  GloVe is an unsupervised learning algorithm for obtaining vector representations for words.
  Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and
  the resulting representations showcase interesting linear substructures of the word vector
  space. (Source from https://nlp.stanford.edu/projects/glove/)
  
  References
  ----------
  
  GloVe: Global Vectors for Word Representation.
  Jeffrey Pennington, Richard Socher, and Christopher D. Manning.
  https://nlp.stanford.edu/pubs/glove.pdf
  
  Website:
  
  https://nlp.stanford.edu/projects/glove/
  
  To get the updated URLs to the externally hosted pre-trained token embedding
  files, visit https://nlp.stanford.edu/projects/glove/
  
  License for pre-trained embeddings:
  
      https://fedoraproject.org/wiki/Licensing/PDDL
  
  
  Parameters
  ----------
  pretrained_file_name : str, default 'glove.840B.300d.txt'
      The name of the pre-trained token embedding file.
  embedding_root : str, default $MXNET_HOME/embeddings
      The root directory for storing embedding-related files.
  init_unknown_vec : callback
      The callback used to initialize the embedding vector for the unknown token.
  vocabulary : :class:`~mxnet.contrib.text.vocab.Vocabulary`, default None
      It contains the tokens to index. Each indexed token will be associated with the loaded
      embedding vectors, such as loaded from a pre-trained token embedding file. If None, all the
      tokens from the loaded embedding vectors, such as loaded from a pre-trained token embedding
      file, will be indexed.
class_docstr_tok: ["The","GloVe","word","embedding",".","GloVe","is","an","unsupervised","learning","algorithm","for","obtaining","vector","representations","for","words",".","Training","is","performed","on","aggregated","global","word-word","co-occurrence","statistics","from","a","corpus",",","and","the","resulting","representations","showcase","interesting","linear","substructures","of","the","word","vector","space",".","(","Source","from","https",":","\/\/nlp.stanford.edu\/projects\/glove\/",")","References","--","--","--","--","--","GloVe",":","Global","Vectors","for","Word","Representation",".","Jeffrey","Pennington",",","Richard","Socher",",","and","Christopher","D.","Manning",".","https",":","\/\/nlp.stanford.edu\/pubs\/glove.pdf","Website",":","https",":","\/\/nlp.stanford.edu\/projects\/glove\/","To","get","the","updated","URLs","to","the","externally","hosted","pre-trained","token","embedding","files",",","visit","https",":","\/\/nlp.stanford.edu\/projects\/glove\/","License","for","pre-trained","embeddings",":","https",":","\/\/fedoraproject.org\/wiki\/Licensing\/PDDL","Parameters","--","--","--","--","--","pretrained_file_name",":","str",",","default","'glove.840B.300d.txt'","The","name","of","the","pre-trained","token","embedding","file",".","embedding_root",":","str",",","default","$","MXNET_HOME\/embeddings","The","root","directory","for","storing","embedding-related","files",".","init_unknown_vec",":","callback","The","callback","used","to","initialize","the","embedding","vector","for","the","unknown","token",".","vocabulary",":",":","class",":","`","~mxnet.contrib.text.vocab.Vocabulary","`",",","default","None","It","contains","the","tokens","to","index",".","Each","indexed","token","will","be","associated","with","the","loaded","embedding","vectors",",","such","as","loaded","from","a","pre-trained","token","embedding","file",".","If","None",",","all","the","tokens","from","the","loaded","embedding","vectors",",","such","as","loaded","from","a","pre-trained","token","embedding","file",",","will","be","indexed","."]
code: def _get_download_file_name(cls, pretrained_file_name):
      # Map a pre-trained embedding file to its archive to download.
      src_archive = {
          archive.split(".")[1]: archive
          for archive in GloVe.pretrained_archive_name_sha1.keys()
      }
      archive = src_archive[pretrained_file_name.split(".")[1]]
      return archive
  
imports: import logging
  import os
  import tarfile
  import warnings
  import zipfile
  from .None import _constants
  from .None import vocab
  from ...None import ndarray
  from ...None import registry
  from ...None import base
  from ...util import is_np_array
  from ...None import numpy
  from ...None import numpy_extension
label_desc: Use image node_id 1 for calling the GloVe obj's underlying member method code with example usage: obj._get_download_file_name(cls, pretrained_file_name) and returns: archive