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"]
code: def __init__(self, path, language, vocab_size, emb_dim=300):
      self.path = path
      self.language = language
      self.vocab_size = vocab_size
      self.emb_dim = emb_dim
      self.url = (
          "https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki."
          + language
          + ".vec"
      )
  
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 1 to create a new FastTextEmb object  with example: obj = FastTextEmb(path, language, vocab_size, emb_dim)