class_docstr: An sklearn classifier adapter for fasttext that takes input by index.
  
  Parameters
  ----------
  input_ix : int
      The index of the text input column for fasttext.
  **kwargs
      Additional keyword arguments will be redirected to
      fasttext.train_supervised.
class_docstr_tok: ["An","sklearn","classifier","adapter","for","fasttext","that","takes","input","by","index",".","Parameters","--","--","--","--","--","input_ix",":","int","The","index","of","the","text","input","column","for","fasttext",".","*","*","kwargs","Additional","keyword","arguments","will","be","redirected","to","fasttext.train_supervised","."]
code: def __init__(self, input_ix, **kwargs):
      super().__init__(**kwargs)
      self.input_ix = input_ix
  
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 1 to create a new IdxBasedFtClassifier object from inherited base classes: FtClassifierABC with example: obj = IdxBasedFtClassifier(input_ix)