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.
class_docstr_tok: ["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","."]
code: def __deepcopy__(self, memo):
      if self.clf is None:
          self_clf_copy = None
      else:
          fn = "tmp_{}.fasttext.model".format(int(time.time()))
          self.clf.save_model(fn)
          self_clf_copy = load_model(fn)
          os.remove(fn)
      # Store self.clf
      params = self.__dict__
      clf = params.pop("clf")
      # Copy params without self.clf (it can't be copied)
      params_copy = copy.deepcopy(params)
      # Add clf back to self.clf
      self.clf = clf
      # Create copy to return
      clf_copy = FastTextClassifier(self.train_data_fn)
      params_copy["clf"] = self_clf_copy
      clf_copy.__dict__ = params_copy
      return clf_copy
  
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 4 for calling the FastTextClassifier obj's underlying member method code with example usage: obj.__deepcopy__(memo) and returns: clf_copy