class_docstr: Abstract base class for time series regressors.
  
  The base regressor specifies the methods and method signatures that all
  regressors have to implement. Attributes with a underscore suffix are set in the
  method fit.
  
  Parameters
  ----------
  fit_time_           : integer, time (in milliseconds) for fit to run.
  _class_dictionary   : dictionary mapping classes_ onto integers 0...n_classes_-1.
  _threads_to_use     : number of threads to use in fit as determined by n_jobs.
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docstr: Predicts labels for sequences in X.
  
  Parameters
  ----------
  X : sktime compatible time series panel data container, Panel scitype, e.g.,
      pd-multiindex: pd.DataFrame with columns = variables,
      index = pd.MultiIndex with first level = instance indices,
      second level = time indices
      numpy3D: 3D np.array (any number of dimensions, equal length series)
      of shape [n_instances, n_dimensions, series_length]
      or of any other supported Panel mtype
      for list of mtypes, see datatypes.SCITYPE_REGISTER
      for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
  Returns
  -------
  y_pred : sktime compatible tabular data container, Table scitype
      1D iterable, of shape [n_instances]
      or 2D iterable, of shape [n_instances, n_dimensions]
      predicted class labels
      0-th indices correspond to instance indices in X
      1-st indices (if applicable) correspond to multioutput vector indices in X
      1D np.npdarray, if y univariate (one dimension)
      otherwise, same type as y passed in fit
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code: def predict(self, X) -> np.ndarray:
      """Predicts labels for sequences in X.
  
      Parameters
      ----------
      X : sktime compatible time series panel data container, Panel scitype, e.g.,
          pd-multiindex: pd.DataFrame with columns = variables,
          index = pd.MultiIndex with first level = instance indices,
          second level = time indices
          numpy3D: 3D np.array (any number of dimensions, equal length series)
          of shape [n_instances, n_dimensions, series_length]
          or of any other supported Panel mtype
          for list of mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
      Returns
      -------
      y_pred : sktime compatible tabular data container, Table scitype
          1D iterable, of shape [n_instances]
          or 2D iterable, of shape [n_instances, n_dimensions]
          predicted class labels
          0-th indices correspond to instance indices in X
          1-st indices (if applicable) correspond to multioutput vector indices in X
          1D np.npdarray, if y univariate (one dimension)
          otherwise, same type as y passed in fit
      """
      self.check_is_fitted()
  
      # enter vectorized mode if needed
      if self._is_vectorized:
          return self._vectorize("predict", X=X)
  
      # boilerplate input checks for predict-like methods
      X = self._check_convert_X_for_predict(X)
  
      # call internal _predict, convert output
      y_pred_inner = self._predict(X)
      y_pred = self._convert_output_y(y_pred_inner)
      return y_pred
  
file_path: sktime/sktime/regression/base.py
imports: import time
  import numpy
  from sktime.base import BasePanelMixin
  from sktime.datatypes import VectorizedDF
  from sktime.utils.sklearn import is_sklearn_transformer
  from sktime.utils.validation import check_n_jobs
label_desc: Use image node_id 4 for calling the BaseRegressor obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred, self