class_docstr: Handles parameter management for estimators composed of named estimators.
  
  Partly adapted from sklearn utils.metaestimator.py.
docstr: Set the parameters of estimator.
  
  Valid parameter keys can be listed with ``get_params()``.
  
  Returns
  -------
  self : returns an instance of self.
docstr_tok: ["Set","the","parameters","of","estimator",".","Valid","parameter","keys","can","be","listed","with","``","get_params","(",")","``",".","Returns","--","--","--","-","self",":","returns","an","instance","of","self","."]
code: def set_params(self, **kwargs):
      """Set the parameters of estimator.
  
      Valid parameter keys can be listed with ``get_params()``.
  
      Returns
      -------
      self : returns an instance of self.
      """
      steps_attr = self._steps_attr
      self._set_params(steps_attr, **kwargs)
      return self
  
imports: from inspect import isclass
  import numpy
  import pandas
  from sktime.base import BaseEstimator
label_desc: Use image node_id 2 for calling the _HeterogenousMetaEstimator obj's underlying member method code with example usage: obj.set_params() and returns: self