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","."]
code: def _fit_input_col(
      self,
      input_col,
      y,
      input_col_validation=None,
      y_validation=None,
  ):
      # Store the classes seen during fit
      self.classes_ = unique_labels(y)
      self.num_classes_ = len(self.classes_)
      self.class_labels_ = [
          "__label__{}".format(lbl) for lbl in self.classes_
      ]
      # Dump training set to a fasttext-compatible file
      temp_trainset_fpath = temp_dataset_fpath()
      dump_xy_to_fasttext_format(input_col, y, temp_trainset_fpath)
      if input_col_validation is not None:
          n_classes_validation = len(unique_labels(y_validation))
          assert n_classes_validation == self.num_classes_, (
              "Number of validation classes doesn't match number of "
              "training classes"
          )
          temp_trainset_fpath_validation = temp_dataset_fpath()
          dump_xy_to_fasttext_format(
              input_col_validation,
              y_validation,
              temp_trainset_fpath_validation,
          )
          # train
          self.model = train_supervised(
              input=temp_trainset_fpath,
              **{
                  "autotuneValidationFile": temp_trainset_fpath_validation,
                  **self.kwargs,
              },
          )
          try:
              os.remove(temp_trainset_fpath_validation)
          except FileNotFoundError:  # pragma: no cover
              pass
      else:
          self.model = train_supervised(
              input=temp_trainset_fpath, **self.kwargs
          )
  
      # Return the classifier
      try:
          os.remove(temp_trainset_fpath)
      except FileNotFoundError:  # pragma: no cover
          pass
  
      return self
  
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 9 for calling the FtClassifierABC obj's underlying member method code with example usage: obj._fit_input_col(input_col, y, input_col_validation, y_validation) and returns: self