code: def train_supervised(*kargs, **kwargs):
      """
      Train a supervised 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 file must must contain at least one label per line. For an
      example consult the example datasets which are part of the fastText
      repository such as the dataset pulled by classification-example.sh.
      """
      supervised_default = unsupervised_default.copy()
      supervised_default.update(
          {
              "lr": 0.1,
              "minCount": 1,
              "minn": 0,
              "maxn": 0,
              "loss": "softmax",
              "model": "supervised",
          }
      )
  
      arg_names = [
          "input",
          "lr",
          "dim",
          "ws",
          "epoch",
          "minCount",
          "minCountLabel",
          "minn",
          "maxn",
          "neg",
          "wordNgrams",
          "loss",
          "bucket",
          "thread",
          "lrUpdateRate",
          "t",
          "label",
          "verbose",
          "pretrainedVectors",
          "seed",
          "autotuneValidationFile",
          "autotuneMetric",
          "autotunePredictions",
          "autotuneDuration",
          "autotuneModelSize",
      ]
      args, manually_set_args = read_args(
          kargs, kwargs, arg_names, supervised_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 7 for calling a global function with example usage: train_supervised() and returns: ft