class_docstr: Delegator mixin that delegates all methods to wrapped regressor.
  
  Delegates inner regressor methods to a wrapped estimator.
      Wrapped estimator is value of attribute with name self._delegate_name.
      By default, this is "estimator_", i.e., delegates to self.estimator_
      To override delegation, override _delegate_name attribute in child class.
  
  Delegates the following inner underscore methods:
      _fit, _predict, _predict_proba
  
  Does NOT delegate get_params, set_params.
      get_params, set_params will hence use one additional nesting level by default.
  
  Does NOT delegate or copy tags, this should be done in a child class if required.
docstr: Predict labels for sequences in X.
  
  private _predict containing the core logic, called from predict
  
  State required:
      Requires state to be "fitted".
  
  Accesses in self:
      Fitted model attributes ending in "_"
  
  Parameters
  ----------
  X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
      if self.get_tag("X_inner_mtype") = "numpy3D":
          3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
      if self.get_tag("X_inner_mtype") = "nested_univ":
          pd.DataFrame with each column a dimension, each cell a pd.Series
      for list of other mtypes, see datatypes.SCITYPE_REGISTER
      for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
  Returns
  -------
  y : 1D np.array of float, of shape [n_instances] - predicted regression labels
      indices correspond to instance indices in X
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code: def _predict(self, X):
      """Predict labels for sequences in X.
  
      private _predict containing the core logic, called from predict
  
      State required:
          Requires state to be "fitted".
  
      Accesses in self:
          Fitted model attributes ending in "_"
  
      Parameters
      ----------
      X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
          if self.get_tag("X_inner_mtype") = "numpy3D":
              3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
          if self.get_tag("X_inner_mtype") = "nested_univ":
              pd.DataFrame with each column a dimension, each cell a pd.Series
          for list of other mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
      Returns
      -------
      y : 1D np.array of float, of shape [n_instances] - predicted regression labels
          indices correspond to instance indices in X
      """
      estimator = self._get_delegate()
      return estimator.predict(X=X)
  
imports: from sktime.regression.base import BaseRegressor
label_desc: Use image node_id 3 for calling the _DelegatedRegressor obj's underlying member method code with example usage: obj._predict(X) and returns: estimator