class_docstr: Handles parameter management for estimators composed of named estimators.
  
  Partly adapted from sklearn utils.metaestimator.py.
docstr: Get parameters of estimator.
  
  Parameters
  ----------
  deep : boolean, optional
      If True, will return the parameters for this estimator and
      contained sub-objects that are estimators.
  
  Returns
  -------
  params : mapping of string to any
      Parameter names mapped to their values.
docstr_tok: ["Get","parameters","of","estimator",".","Parameters","--","--","--","--","--","deep",":","boolean",",","optional","If","True",",","will","return","the","parameters","for","this","estimator","and","contained","sub-objects","that","are","estimators",".","Returns","--","--","--","-","params",":","mapping","of","string","to","any","Parameter","names","mapped","to","their","values","."]
code: def get_params(self, deep=True):
      """Get parameters of estimator.
  
      Parameters
      ----------
      deep : boolean, optional
          If True, will return the parameters for this estimator and
          contained sub-objects that are estimators.
  
      Returns
      -------
      params : mapping of string to any
          Parameter names mapped to their values.
      """
      steps = self._steps_attr
      return self._get_params(steps, deep=deep)
  
imports: from inspect import isclass
  import numpy
  import pandas
  from sktime.base import BaseEstimator
label_desc: Use image node_id 1 for calling the _HeterogenousMetaEstimator obj's underlying member method code with example usage: obj.get_params(deep) and returns: self