code: def train_unsupervised(*kargs, **kwargs):
      """
      Train an unsupervised model and return a model object.
  
      input must be a filepath. The input text does not need to be tokenized
      as per the tokenize function, but it must be preprocessed and encoded
      as UTF-8. You might want to consult standard preprocessing scripts such
      as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html
  
      The input field must not contain any labels or use the specified label prefix
      unless it is ok for those words to be ignored. For an example consult the
      dataset pulled by the example script word-vector-example.sh, which is
      part of the fastText repository.
      """
      arg_names = [
          "input",
          "model",
          "lr",
          "dim",
          "ws",
          "epoch",
          "minCount",
          "minCountLabel",
          "minn",
          "maxn",
          "neg",
          "wordNgrams",
          "loss",
          "bucket",
          "thread",
          "lrUpdateRate",
          "t",
          "label",
          "verbose",
          "pretrainedVectors",
      ]
      args, manually_set_args = read_args(
          kargs, kwargs, arg_names, unsupervised_default
      )
      a = _build_args(args, manually_set_args)
      ft = _FastText(args=a)
      fasttext.train(ft.f, a)
      ft.set_args(ft.f.getArgs())
      return ft
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import fasttext_pybind
  import numpy
  import multiprocessing
  import sys
  from itertools import chain
label_desc: Use image node_id 8 for calling a global function with example usage: train_unsupervised() and returns: ft