class_docstr: Base forecaster template class.
  
  The base forecaster specifies the methods and method signatures that all forecasters
  have to implement.
  
  Specific implementations of these methods is deferred to concrete forecasters.
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docstr: Magic [...] method, return forecaster with subsetted data.
  
  First index does subsetting of exogeneous input data.
  Second index does subsetting of the forecast (but not of endogeneous data).
  
  Keys must be valid inputs for `columns` in `ColumnSelect`.
  
  Parameters
  ----------
  key: valid input for `columns` in `ColumnSelect`, or pair thereof
      keys can also be a :-slice, in which case it is considered as not passed
  
  Returns
  -------
  the following composite pipeline object:
      ColumnSelect(columns1) ** self * ColumnSelect(columns2)
      where `columns1` is first or only item in `key`, and `columns2` is the last
      if only one item is passed in `key`, only `columns1` is applied to input
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code: def __getitem__(self, key):
      """Magic [...] method, return forecaster with subsetted data.
  
      First index does subsetting of exogeneous input data.
      Second index does subsetting of the forecast (but not of endogeneous data).
  
      Keys must be valid inputs for `columns` in `ColumnSelect`.
  
      Parameters
      ----------
      key: valid input for `columns` in `ColumnSelect`, or pair thereof
          keys can also be a :-slice, in which case it is considered as not passed
  
      Returns
      -------
      the following composite pipeline object:
          ColumnSelect(columns1) ** self * ColumnSelect(columns2)
          where `columns1` is first or only item in `key`, and `columns2` is the last
          if only one item is passed in `key`, only `columns1` is applied to input
      """
      from sktime.transformations.series.subset import ColumnSelect
  
      def is_noneslice(obj):
          res = isinstance(obj, slice)
          res = (
              res
              and obj.start is None
              and obj.stop is None
              and obj.step is None
          )
          return res
  
      if isinstance(key, tuple):
          if not len(key) == 2:
              raise ValueError(
                  "there should be one or two keys when calling [] or getitem, "
                  "of a forecaster, "
                  "e.g., mytrafo[key], or mytrafo[key1, key2]. "
                  f"But {self.__class__.__name__} instance got tuple"
                  f" with {len(key)} keys."
              )
          columns1 = key[0]
          columns2 = key[1]
          if is_noneslice(columns1) and is_noneslice(columns2):
              return self
          elif is_noneslice(columns2):
              return ColumnSelect(columns1) ** self
          elif is_noneslice(columns1):
              return self * ColumnSelect(columns2)
          else:
              return ColumnSelect(columns1) ** self * ColumnSelect(
                  columns2
              )
      else:
          return ColumnSelect(key) ** self
  
imports: from copy import deepcopy
  from itertools import product
  import numpy
  import pandas
  from sktime.base import BaseEstimator
  from sktime.datatypes import VectorizedDF, check_is_scitype, convert_to, get_cutoff, mtype_to_scitype, scitype_to_mtype, update_data
  from sktime.forecasting.base._fh import ForecastingHorizon
  from sktime.utils.datetime import _shift
  from sktime.utils.validation._dependencies import _check_estimator_deps
  from sktime.utils.validation.forecasting import check_alpha, check_cv, check_fh, check_X
  from sktime.utils.validation.series import check_equal_time_index
  from sktime.utils.warnings import warn
label_desc: Use image node_id 6 for calling the BaseForecaster obj's underlying member method code with example usage: obj.__getitem__(key) and returns: res, unknown, self, unknown, unknown, unknown