class_docstr: Base class for forests of trees.
docstr: Make and configure a copy of the `_estimator` attribute.
  
  Warning: This method should be used to properly instantiate new
  sub-estimators.
docstr_tok: ["Make","and","configure","a","copy","of","the","`","_estimator","`","attribute",".","Warning",":","This","method","should","be","used","to","properly","instantiate","new","sub-estimators","."]
code: def _make_estimator(self, append=True, random_state=None):
      """Make and configure a copy of the `_estimator` attribute.
  
      Warning: This method should be used to properly instantiate new
      sub-estimators.
      """
      estimator = clone(self._estimator)
      estimator.set_params(
          **{p: getattr(self, p) for p in self.estimator_params}
      )
  
      if random_state is not None:
          set_random_state(estimator, random_state)
  
      if append:
          self.estimators_.append(estimator)
  
      return estimator
  
imports: from abc import abstractmethod
  from warnings import catch_warnings, simplefilter
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from numpy import float64
  from sklearn.base import clone
  from sklearn.ensemble._forest import MAX_INT, BaseForest, _generate_sample_indices, _get_n_samples_bootstrap
  from sklearn.exceptions import DataConversionWarning
  from sklearn.utils import check_array, check_random_state, compute_sample_weight
  from sktime.transformations.panel.summarize import RandomIntervalFeatureExtractor
  from sktime.utils.random_state import set_random_state
  from sktime.utils.warnings import warn
label_desc: Use image node_id 2 for calling the BaseTimeSeriesForest obj's underlying member method code with example usage: obj._make_estimator(append, random_state) and returns: estimator