class_docstr: Fasttext embedding trainer class
  
  Args:
      texts (List[List[str]]): list of tokenized sentences
      size (int): embedding size
      epochs (int, optional): number of epochs to train
      window (int, optional): The maximum distance between
      the current and predicted word within a sentence
class_docstr_tok: ["Fasttext","embedding","trainer","class","Args",":","texts","(","List","[","List","[","str","]","]",")",":","list","of","tokenized","sentences","size","(","int",")",":","embedding","size","epochs","(","int",",","optional",")",":","number","of","epochs","to","train","window","(","int",",","optional",")",":","The","maximum","distance","between","the","current","and","predicted","word","within","a","sentence"]
docstr: return vector corresponding given word
docstr_tok: ["return","vector","corresponding","given","word"]
code: def vec(self, word: str) -> np.ndarray:
      """return vector corresponding given word"""
      return self.model.wv[word]
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import sys
  from typing import List
  import numpy
  from gensim.models import FastText
  from nlp_architect.utils.text import Vocabulary
label_desc: Use image node_id 3 for calling the FasttextEmbeddingsModel obj's underlying member method code with example usage: obj.vec(word) and returns: self