class_docstr: Base Class for column ensemble.
class_docstr_tok: ["Base","Class","for","column","ensemble","."]
docstr: Fit all estimators, fit the data.
  
  Parameters
  ----------
  X : array-like or DataFrame of shape [n_samples, n_dimensions,
  n_length]
      Input data, of which specified subsets are used to fit the
      transformations.
  
  y : array-like, shape (n_samples, ...), optional
      Targets for supervised learning.
docstr_tok: ["Fit","all","estimators",",","fit","the","data",".","Parameters","--","--","--","--","--","X",":","array-like","or","DataFrame","of","shape","[","n_samples",",","n_dimensions",",","n_length","]","Input","data",",","of","which","specified","subsets","are","used","to","fit","the","transformations",".","y",":","array-like",",","shape","(","n_samples",",","...",")",",","optional","Targets","for","supervised","learning","."]
code: def _fit(self, X, y):
      # the data passed in could be an array of dataframes?
      """Fit all estimators, fit the data.
  
      Parameters
      ----------
      X : array-like or DataFrame of shape [n_samples, n_dimensions,
      n_length]
          Input data, of which specified subsets are used to fit the
          transformations.
  
      y : array-like, shape (n_samples, ...), optional
          Targets for supervised learning.
      """
      if self.estimators is None or len(self.estimators) == 0:
          raise AttributeError(
              "Invalid `estimators` attribute, `estimators`"
              " should be a list of (string, estimator)"
              " tuples"
          )
  
      # X = _check_X(X)
      self._validate_estimators()
      self._validate_column_callables(X)
      self._validate_remainder(X)
  
      self.le_ = LabelEncoder().fit(y)
      self.classes_ = self.le_.classes_
      transformed_y = self.le_.transform(y)
  
      estimators_ = []
      for name, estimator, column in self._iter(replace_strings=True):
          estimator = estimator.clone()
          estimator.fit(_get_column(X, column), transformed_y)
          estimators_.append((name, estimator, column))
  
      self.estimators_ = estimators_
      return self
  
imports: from itertools import chain
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from sktime.base import _HeterogenousMetaEstimator
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 8 for calling the BaseColumnEnsembleClassifier obj's underlying member method code with example usage: obj._fit(X, y) and returns: self