class_docstr: Downloads FastText Embeddings for a given language to the given path.
  Arguments:
      path(str): Local path to copy embeddings
      language(str): Embeddings language
      vocab_size(int): Size of vocabulary
  Returns:
      Returns a dictionary and reverse dictionary
      Returns a numpy array with embeddings in emb_sizexvocab_size shape
class_docstr_tok: ["Downloads","FastText","Embeddings","for","a","given","language","to","the","given","path",".","Arguments",":","path","(","str",")",":","Local","path","to","copy","embeddings","language","(","str",")",":","Embeddings","language","vocab_size","(","int",")",":","Size","of","vocabulary","Returns",":","Returns","a","dictionary","and","reverse","dictionary","Returns","a","numpy","array","with","embeddings","in","emb_sizexvocab_size","shape"]
docstr: Download filename from url unless it's already in directory
docstr_tok: ["Download","filename","from","url","unless","it","'s","already","in","directory"]
code: def _maybe_download(self):
      """
      Download filename from url unless it's already in directory
      """
      # 1. Check if the file doesnt exist. Download and extract if it doesnt
      filename = "wiki." + self.language + ".vec"
      filepath = os.path.join(self.path, filename)
      link = "https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md"
      if not os.path.exists(filepath):
          if license_prompt(filepath, link, self.path):
              print(
                  "Downloading FastText embeddings for "
                  + self.language
                  + " to "
                  + filepath
              )
              urllib.request.urlretrieve(self.url, filepath)
              statinfo = os.stat(filepath)
              print(
                  "Sucessfully downloaded",
                  filename,
                  statinfo.st_size,
                  "bytes",
              )
          else:
              exit()
      else:
          print(
              "Found FastText embeddings for "
              + self.language
              + " at "
              + filepath
          )
      return filepath
  
file_path: nlp-architect/nlp_architect/data/fasttext_emb.py
imports: import os
  from six.moves import urllib
  import numpy
  from nlp_architect.utils.generic import license_prompt
label_desc: Use image node_id 2 for calling the FastTextEmb obj's underlying member method code with example usage: obj._maybe_download() and returns: filepath