code: def train_supervised(
      input,
      lr=0.1,
      dim=100,
      ws=5,
      epoch=5,
      minCount=1,
      minCountLabel=0,
      minn=0,
      maxn=0,
      neg=5,
      wordNgrams=1,
      loss="softmax",
      bucket=2000000,
      thread=multiprocessing.cpu_count() - 1,
      lrUpdateRate=100,
      t=1e-4,
      label="__label__",
      verbose=2,
      pretrainedVectors="",
  ):
      
      model = "supervised"
      a = _build_args(locals())
      ft = _FastText()
      fasttext.train(ft.f, a)
      return ft
docs: 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.