code: def get_word_vector(data, model):
      t1 = time.time()
      print("Reading")
      with open(data, "r") as f:
          tokens = tokenize(f.read())
      t2 = time.time()
      print("Read TIME: " + str(t2 - t1))
      print("Read NUM : " + str(len(tokens)))
      f = load_model(model)
      # This is not equivalent to piping the data into
      # print-word-vector, because the data is tokenized
      # first.
      t3 = time.time()
      i = 0
      for t in tokens:
          f.get_word_vector(t)
          i += 1
          if i % 10000 == 0:
              sys.stderr.write("\ri: " + str(float(i / len(tokens))))
              sys.stderr.flush()
      t4 = time.time()
      print("\nVectoring: " + str(t4 - t3))
  
file_path: fastText/python/benchmarks/get_word_vector.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from fasttext import load_model
  from fasttext import tokenize
  import sys
  import time
  import tempfile
  import argparse
label_desc: Use image node_id 1 for calling a global function with example usage: get_word_vector(data, model) without return types