class_docstr: Handles parameter management for estimators composed of named estimators.
  
  Partly adapted from sklearn utils.metaestimator.py.
docstr: Set self's `tag_name` tag to `value` if any estimator on the list has it.
  
  Writes to self:
  sets the tag `tag_name` to `value` if `_anytagis(tag_name, value)` is True
      otherwise sets the tag `tag_name` to `value_if_not`
  
  Parameters
  ----------
  tag_name : str, name of the tag
  value : value to check and to set tag to if one of the tag values is `value`
  value_if_not : value to set in self if none of the tag values is `value`
  estimators : list of (str, estimator) pairs to query for the tag/value
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code: def _anytagis_then_set(
      self, tag_name, value, value_if_not, estimators
  ):
      """Set self's `tag_name` tag to `value` if any estimator on the list has it.
  
      Writes to self:
      sets the tag `tag_name` to `value` if `_anytagis(tag_name, value)` is True
          otherwise sets the tag `tag_name` to `value_if_not`
  
      Parameters
      ----------
      tag_name : str, name of the tag
      value : value to check and to set tag to if one of the tag values is `value`
      value_if_not : value to set in self if none of the tag values is `value`
      estimators : list of (str, estimator) pairs to query for the tag/value
      """
      if self._anytagis(
          tag_name=tag_name, value=value, estimators=estimators
      ):
          self.set_tags(**{tag_name: value})
      else:
          self.set_tags(**{tag_name: value_if_not})
  
imports: from inspect import isclass
  import numpy
  import pandas
  from sktime.base import BaseEstimator
label_desc: Use image node_id 19 for calling the _HeterogenousMetaEstimator obj's underlying member method code with example usage: obj._anytagis_then_set(tag_name, value, value_if_not, estimators) without return types