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: Get parameters for this estimator.
  
  Parameters
  ----------
  deep : boolean, optional
      If True, will return the parameters for this estimator and
      contained subobjects that are estimators.
  Returns
  -------
  params : mapping of string to any
      Parameter names mapped to their values.
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code: def get_params(self, deep=True):
      """Get parameters for this estimator.
  
      Parameters
      ----------
      deep : boolean, optional
          If True, will return the parameters for this estimator and
          contained subobjects that are estimators.
      Returns
      -------
      params : mapping of string to any
          Parameter names mapped to their values.
      """
      # re-implementation that will preserve ft kwargs
      # if len(self.kwargs) > 1:
      #     return self.kwargs
      # return unsupervised_default.copy()
      return self.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 4 for calling the FtClassifierABC obj's underlying member method code with example usage: obj.get_params(deep) and returns: self