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: Return column names for DataFrame-like returns.
  
  Primarily used as helper for probabilistic predict-like methods.
  Assumes that _check_X_y has been called, and self._y_metadata set.
  
  Parameter
  ---------
  method : str, optional (default="predict")
      method for which to return column names
      one of "predict", "predict_interval", "predict_quantiles", "predict_var"
  kwargs : dict
      additional keyword arguments passed to private method
      important: args to private method, e.g., _predict, _predict_interval
  
  Returns
  -------
  columns : pd.Index
      column names
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code: def _get_columns(self, method="predict", **kwargs):
      """Return column names for DataFrame-like returns.
  
      Primarily used as helper for probabilistic predict-like methods.
      Assumes that _check_X_y has been called, and self._y_metadata set.
  
      Parameter
      ---------
      method : str, optional (default="predict")
          method for which to return column names
          one of "predict", "predict_interval", "predict_quantiles", "predict_var"
      kwargs : dict
          additional keyword arguments passed to private method
          important: args to private method, e.g., _predict, _predict_interval
  
      Returns
      -------
      columns : pd.Index
          column names
      """
      featnames = self._get_varnames()
  
      if method in ["predict", "predict_var"]:
          return featnames
      else:
          assert method in ["predict_interval", "predict_quantiles"]
  
      if method == "predict_interval":
          coverage = kwargs.get("coverage", None)
          if coverage is None:
              raise ValueError(
                  "coverage must be passed to _get_columns for predict_interval"
              )
          return pd.MultiIndex.from_product(
              [featnames, coverage, ["lower", "upper"]]
          )
  
      if method == "predict_quantiles":
          alpha = kwargs.get("alpha", None)
          if alpha is None:
              raise ValueError(
                  "alpha must be passed to _get_columns for predict_quantiles"
              )
          return pd.MultiIndex.from_product([featnames, alpha])
  
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 40 for calling the BaseForecaster obj's underlying member method code with example usage: obj._get_columns(method) and returns: featnames, pd, pd