class_docstr: Base Class for column ensemble.
class_docstr_tok: ["Base","Class","for","column","ensemble","."]
docstr: Generate (name, estimator, column) tuples.
  
  If fitted=True, use the fitted transformations, else use the user specified
  transformations updated with converted column names and potentially appended
  with transformer for remainder.
docstr_tok: ["Generate","(","name",",","estimator",",","column",")","tuples",".","If","fitted=True",",","use","the","fitted","transformations",",","else","use","the","user","specified","transformations","updated","with","converted","column","names","and","potentially","appended","with","transformer","for","remainder","."]
code: def _iter(self, replace_strings=False):
      """Generate (name, estimator, column) tuples.
  
      If fitted=True, use the fitted transformations, else use the user specified
      transformations updated with converted column names and potentially appended
      with transformer for remainder.
      """
      if self.is_fitted:
          estimators = self.estimators_
      else:
          # interleave the validated column specifiers
          estimators = [
              (name, estimator, column)
              for (name, estimator, _), column in zip(
                  self.estimators, self._columns
              )
          ]
  
      # add transformer tuple for remainder
      if self._remainder[2] is not None:
          estimators = chain(estimators, [self._remainder])
  
      for name, estimator, column in estimators:
          if replace_strings:
              # skip in case of 'drop'
              if estimator == "drop":
                  continue
              elif _is_empty_column_selection(column):
                  continue
  
          yield name, estimator, column
  
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 7 for calling the BaseColumnEnsembleClassifier obj's underlying member method code with example usage: obj._iter(replace_strings) without return types