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 load_embeddings(self):
      # Check if embeddings exist else download
      filepath = self._maybe_download()
      # Read embeddings
      dico, word_vec = self.read_embeddings(filepath)
      print("Completed loading embeddings for " + self.language)
      word_vec = np.float32(word_vec)
      return dico, word_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 4 for calling the FastTextEmb obj's underlying member method code with example usage: obj.load_embeddings() and returns: dico,  word_vec