docstr: Arguments:
     language: Language of fastText pre-trained embedding model
     cache: directory for cached model
   
docstr_tok: ["Arguments",":","language",":","Language","of","fastText","pre-trained","embedding","model","cache",":","directory","for","cached","model"]
code: def __init__(self, language="en", url_base=None, cache=None):
      """
      Arguments:
         language: Language of fastText pre-trained embedding model
         cache: directory for cached model
      """
      cache = os.path.expanduser(cache)
      base = (
          url_base
          or "https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.{}.zip"
      )
      url = base.format(language)
      backup_url = None
      self.destination = os.path.join(
          cache, "wiki.{}.zip".format(language)
      )
      if language == "en" and url_base is None:
          backup_url = url
          self.backup_destination = self.destination
          url = FastTextBinary._direct_en_url
          self.destination = os.path.join(cache, "wiki.en.bin")
      name = FastTextBinary.name_base.format(language)
  
      self.cache(name, cache, url=url, backup_url=backup_url)
  
imports: import gzip
  import logging
  import os
  import shutil
  import tarfile
  import zipfile
  import nltk
  import six
  import fasttext
  import torch
  from torchtext.legacy import data, vocab
  from torchtext.utils import download_from_url
  from urllib.request import urlretrieve
label_desc: Use image node_id 1 to create a new FastTextBinary object from inherited base classes: vocab with example: obj = FastTextBinary(language, url_base, cache)