class_docstr: The fastText word embedding.
  
  
  FastText is an open-source, free, lightweight library that allows users to learn text
  representations and text classifiers. It works on standard, generic hardware. Models can later
  be reduced in size to even fit on mobile devices. (Source from https://fasttext.cc/)
  
  References
  ----------
  
  Enriching Word Vectors with Subword Information.
  Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov.
  https://arxiv.org/abs/1607.04606
  
  Bag of Tricks for Efficient Text Classification.
  Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov.
  https://arxiv.org/abs/1607.01759
  
  FastText.zip: Compressing text classification models.
  Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou,
  and Tomas Mikolov.
  https://arxiv.org/abs/1612.03651
  
  For 'wiki.multi' embeddings:
  Word Translation Without Parallel Data
  Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer,
  and Herve Jegou.
  https://arxiv.org/abs/1710.04087
  
  Website:
  
  https://fasttext.cc/
  
  To get the updated URLs to the externally hosted pre-trained token embedding files, visit
  https://github.com/facebookresearch/fastText/blob/master/docs/pretrained-vectors.md
  
  License for pre-trained embeddings:
  
      https://creativecommons.org/licenses/by-sa/3.0/
  
  
  Parameters
  ----------
  pretrained_file_name : str, default 'wiki.en.vec'
      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","fastText","word","embedding",".","FastText","is","an","open-source",",","free",",","lightweight","library","that","allows","users","to","learn","text","representations","and","text","classifiers",".","It","works","on","standard",",","generic","hardware",".","Models","can","later","be","reduced","in","size","to","even","fit","on","mobile","devices",".","(","Source","from","https",":","\/\/fasttext.cc\/",")","References","--","--","--","--","--","Enriching","Word","Vectors","with","Subword","Information",".","Piotr","Bojanowski",",","Edouard","Grave",",","Armand","Joulin",",","and","Tomas","Mikolov",".","https",":","\/\/arxiv.org\/abs\/1607.04606","Bag","of","Tricks","for","Efficient","Text","Classification",".","Armand","Joulin",",","Edouard","Grave",",","Piotr","Bojanowski",",","and","Tomas","Mikolov",".","https",":","\/\/arxiv.org\/abs\/1607.01759","FastText.zip",":","Compressing","text","classification","models",".","Armand","Joulin",",","Edouard","Grave",",","Piotr","Bojanowski",",","Matthijs","Douze",",","Herve","Jegou",",","and","Tomas","Mikolov",".","https",":","\/\/arxiv.org\/abs\/1612.03651","For","'wiki.multi","'","embeddings",":","Word","Translation","Without","Parallel","Data","Alexis","Conneau",",","Guillaume","Lample",",","Marc'Aurelio","Ranzato",",","Ludovic","Denoyer",",","and","Herve","Jegou",".","https",":","\/\/arxiv.org\/abs\/1710.04087","Website",":","https",":","\/\/fasttext.cc\/","To","get","the","updated","URLs","to","the","externally","hosted","pre-trained","token","embedding","files",",","visit","https",":","\/\/github.com\/facebookresearch\/fastText\/blob\/master\/docs\/pretrained-vectors.md","License","for","pre-trained","embeddings",":","https",":","\/\/creativecommons.org\/licenses\/by-sa\/3.0\/","Parameters","--","--","--","--","--","pretrained_file_name",":","str",",","default","'wiki.en.vec'","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.
      return ".".join(pretrained_file_name.split(".")[:-1]) + ".zip"
  
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 FastText obj's underlying member method code with example usage: obj._get_download_file_name(cls, pretrained_file_name) and returns: unknown