class_docstr: Instantiate a fastText classifier that is compatible with :py:class:`CleanLearning <cleanlab.classification.CleanLearning>`.
  
  Parameters
  ----------
  train_data_fn: str
      File name of the training data in the format compatible with fastText.
  
  test_data_fn: str, optional
      File name of the test data in the format compatible with fastText.
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docstr: Trains the fast text classifier.
  Typical usage requires NO parameters,
  just clf.fit()  # No params.
  
  Parameters
  ----------
  X : iterable, e.g. list, numpy array (default None)
    The list of indices of the data to use.
    When in doubt, set as None. None defaults to range(len(data)).
  y : None
    Leave this as None. It's a filler to suit sklearns reqs.
  sample_weight : None
    Leave this as None. It's a filler to suit sklearns reqs.
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code: def fit(self, X=None, y=None, sample_weight=None):
      """Trains the fast text classifier.
      Typical usage requires NO parameters,
      just clf.fit()  # No params.
  
      Parameters
      ----------
      X : iterable, e.g. list, numpy array (default None)
        The list of indices of the data to use.
        When in doubt, set as None. None defaults to range(len(data)).
      y : None
        Leave this as None. It's a filler to suit sklearns reqs.
      sample_weight : None
        Leave this as None. It's a filler to suit sklearns reqs."""
  
      train_fn = self._create_train_data(data_indices=X)
      self.clf = train_supervised(
          train_fn, **self.kwargs_train_supervised
      )
      self._remove_masked_data(train_fn)
  
file_path: cleanlab/cleanlab/models/fasttext.py
imports: import time
  import os
  import copy
  import numpy
  from sklearn.base import BaseEstimator
  from fasttext import train_supervised, load_model
label_desc: Use image node_id 5 for calling the FastTextClassifier obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) without return types