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: Quantize the model reducing its size and memory footprint.
  
  Accepts and forwards all keyword arguments defined by Python fasttext's
  ``model.quantize`` method. See Python fasttext docymentation:
  https://github.com/facebookresearch/fastText/tree/master/python#model-object
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code: def quantize(self, **kwargs):
      """Quantize the model reducing its size and memory footprint.
  
      Accepts and forwards all keyword arguments defined by Python fasttext's
      ``model.quantize`` method. See Python fasttext docymentation:
      https://github.com/facebookresearch/fastText/tree/master/python#model-object
      """
      self.model.quantize(**kwargs)
  
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 17 for calling the FtClassifierABC obj's underlying member method code with example usage: obj.quantize() without return types