class_docstr: An abstact base class for sklearn classifier adapters for fasttext.
  
  Parameters
  ----------
  **kwargs
      Keyword arguments will be redirected to fasttext.train_supervised.
class_docstr_tok: ["An","abstact","base","class","for","sklearn","classifier","adapters","for","fasttext",".","Parameters","--","--","--","--","--","*","*","kwargs","Keyword","arguments","will","be","redirected","to","fasttext.train_supervised","."]
docstr: Return true if the inner fasttext model is quantized, else False.
docstr_tok: ["Return","true","if","the","inner","fasttext","model","is","quantized",",","else","False","."]
code: def is_quantized(self):
      """Return true if the inner fasttext model is quantized, else False."""
      return self.model.is_quantized()
  
imports: import os
  import abc
  import numpy
  from fasttext import train_supervised
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils.multiclass import unique_labels
  from sklearn.exceptions import NotFittedError
  from .util import temp_dataset_fpath, dump_xy_to_fasttext_format, python_fasttext_model_to_bytes, bytes_to_python_fasttext_model
label_desc: Use image node_id 18 for calling the FtClassifierABC obj's underlying member method code with example usage: obj.is_quantized() and returns: self