class_docstr: Time-Series Forest Regressor.
  
  A time series forest is a meta estimator and an adaptation of the random
  forest for time-series/panel data that fits a number of decision tree
  regressors on various sub-samples of a transformed dataset and uses
  averaging to improve the predictive accuracy and control over-fitting.
  The sub-sample size is always the same as the original input sample size
  but the samples are drawn with replacement if `bootstrap=True` (default).
  
  Parameters
  ----------
  estimator : Pipeline
      A pipeline consisting of series-to-tabular transformations
      and a decision tree regressor as final estimator.
  n_estimators : integer, optional (default=100)
      The number of trees in the forest.
  criterion : string, optional (default="squared_error")
      The function to measure the quality of a split. Supported criteria are
      "squared_error" for the mean squared error, which is equal to variance reduction
      as feature selection criterion and minimizes the L2 loss using the mean of each
      terminal node, "friedman_mse", which uses mean squared error with Friedman's
      improvement score for potential splits, "absolute_error" for the mean absolute
      error, which minimizes the L1 loss using the median of each terminal node,
      and "poisson" which uses reduction in Poisson deviance to find splits.
  max_depth : integer or None, optional (default=None)
      The maximum depth of the tree. If None, then nodes are expanded until
      all leaves are pure or until all leaves contain less than
      min_samples_split samples.
  min_samples_split : int, float, optional (default=2)
      The minimum number of samples required to split an internal node:
      - If int, then consider `min_samples_split` as the minimum number.
      - If float, then `min_samples_split` is a fraction and
        `ceil(min_samples_split * n_samples)` are the minimum
        number of samples for each split.
  min_samples_leaf : int, float, optional (default=1)
      The minimum number of samples required to be at a leaf node.
      A split point at any depth will only be considered if it leaves at
      least ``min_samples_leaf`` training samples in each of the left and
      right branches.  This may have the effect of smoothing the model,
      especially in regression.
      - If int, then consider `min_samples_leaf` as the minimum number.
      - If float, then `min_samples_leaf` is a fraction and
        `ceil(min_samples_leaf * n_samples)` are the minimum
        number of samples for each node.
  min_weight_fraction_leaf : float, optional (default=0.)
      The minimum weighted fraction of the sum total of weights (of all
      the input samples) required to be at a leaf node. Samples have
      equal weight when sample_weight is not provided.
  max_features : int, float, string or None, optional (default="auto")
      The number of features to consider when looking for the best split:
      - If int, then consider `max_features` features at each split.
      - If float, then `max_features` is a fraction and
        `int(max_features * n_features)` features are considered at each
        split.
      - If "auto", then `max_features=sqrt(n_features)`.
      - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
      - If "log2", then `max_features=log2(n_features)`.
      - If None, then `max_features=n_features`.
      Note: the search for a split does not stop until at least one
      valid partition of the node samples is found, even if it requires to
      effectively inspect more than ``max_features`` features.
  max_leaf_nodes : int or None, optional (default=None)
      Grow trees with ``max_leaf_nodes`` in best-first fashion.
      Best nodes are defined as relative reduction in impurity.
      If None then unlimited number of leaf nodes.
  min_impurity_decrease : float, optional (default=0.)
      A node will be split if this split induces a decrease of the impurity
      greater than or equal to this value.
      The weighted impurity decrease equation is the following::
          N_t / N * (impurity - N_t_R / N_t * right_impurity
                              - N_t_L / N_t * left_impurity)
      where ``N`` is the total number of samples, ``N_t`` is the number of
      samples at the current node, ``N_t_L`` is the number of samples in the
      left child, and ``N_t_R`` is the number of samples in the right child.
      ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
      if ``sample_weight`` is passed.
  bootstrap : boolean, optional (default=True)
      Whether bootstrap samples are used when building trees.
  oob_score : bool (default=False)
      Whether to use out-of-bag samples to estimate
      the generalization accuracy.
  n_jobs : int or None, optional (default=None)
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors.
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  verbose : int, optional (default=0)
      Controls the verbosity when fitting and predicting.
  warm_start : bool, optional (default=False)
      When set to ``True``, reuse the solution of the previous call to fit
      and add more estimators to the ensemble, otherwise, just fit a whole
      new forest.
  None, optional (default=None)
      Weights associated with classes in the form ``{class_label: weight}``.
      If not given, all classes are supposed to have weight one. For
      multi-output problems, a list of dicts can be provided in the same
      order as the columns of y.
      Note that for multioutput (including multilabel) weights should be
      defined for each class of every column in its own dict. For example,
      for four-class multilabel classification weights should be
      [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
      [{1:1}, {2:5}, {3:1}, {4:1}].
      The "balanced" mode uses the values of y to automatically adjust
      weights inversely proportional to class frequencies in the input data
      as ``n_samples / (n_classes * np.bincount(y))``
      The "balanced_subsample" mode is the same as "balanced" except that
      weights are computed based on the bootstrap sample for every tree
      grown.
      For multi-output, the weights of each column of y will be multiplied.
      Note that these weights will be multiplied with sample_weight (passed
      through the fit method) if sample_weight is specified.
  
  Attributes
  ----------
  estimators_ : list of DecisionTreeRegressor
      The collection of fitted sub-estimators.
  n_features_ : int
      The number of features when ``fit`` is performed.
  n_outputs_ : int
      The number of outputs when ``fit`` is performed.
  feature_importances_ : array of shape = [n_features]
      The feature importances (the higher, the more important the feature).
  oob_score_ : float
      Score of the training dataset obtained using an out-of-bag estimate.
  oob_decision_function_ : array of shape = [n_samples, n_classes]
      Decision function computed with out-of-bag estimate on the training
      set. If n_estimators is small it might be possible that a data point
      was never left out during the bootstrap. In this case,
      `oob_decision_function_` might contain NaN.
  class_weight: dict, list of dicts, "balanced", "balanced_subsample" or         None, optional (default=None)
      Not needed here, added in the constructor to align with base class             sharing both Classifier and Regressor parameters.
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docstr: Wrap predict_proba to call BaseRegressor.predict_proba.
docstr_tok: ["Wrap","predict_proba","to","call","BaseRegressor.predict_proba","."]
code: def predict_proba(self, X, **kwargs) -> np.ndarray:
      """Wrap predict_proba to call BaseRegressor.predict_proba."""
      return BaseRegressor.predict_proba(self, X=X, **kwargs)
  
imports: import numbers
  import numpy
  from joblib import Parallel, delayed
  from sklearn.ensemble._base import _partition_estimators
  from sklearn.ensemble._forest import _generate_unsampled_indices, _get_n_samples_bootstrap
  from sklearn.metrics import r2_score
  from sklearn.pipeline import Pipeline
  from sklearn.tree import DecisionTreeRegressor
  from sktime.regression.base import BaseRegressor
  from sktime.series_as_features.base.estimators._ensemble import BaseTimeSeriesForest
  from sktime.transformations.panel.summarize import RandomIntervalFeatureExtractor
  from sktime.utils.slope_and_trend import _slope
  from sktime.utils.validation.panel import check_X, check_X_y
  from sktime.utils.warnings import warn
label_desc: Use image node_id 4 for calling the ComposableTimeSeriesForestRegressor obj's underlying member method code with example usage: obj.predict_proba(X) and returns: BaseRegressor