class_docstr: DummyRegressor makes predictions that ignore the input features.
  
  This regressor serves as a simple baseline to compare against other more
  complex regressors.
  The specific behavior of the baseline is selected with the `strategy`
  parameter.
  
  All strategies make predictions that ignore the input feature values passed
  as the `X` argument to `fit` and `predict`. The predictions, however,
  typically depend on values observed in the `y` parameter passed to `fit`.
  
  Function-identical to `sklearn.dummy.DummyRegressor`, which is called
  inside.
  
  Parameters
  ----------
  strategy : {"mean", "median", "quantile", "constant"}, default="mean"
      Strategy to use to generate predictions.
  
      * "mean": always predicts the mean of the training set
      * "median": always predicts the median of the training set
      * "quantile": always predicts a specified quantile of the training set,
      provided with the quantile parameter.
      * "constant": always predicts a constant value that is provided by
      the user.
  
  constant : int or float or array-like of shape (n_outputs,), default=None
      The explicit constant as predicted by the "constant" strategy. This
      parameter is useful only for the "constant" strategy.
  
  quantile : float in [0.0, 1.0], default=None
      The quantile to predict using the "quantile" strategy. A quantile of
      0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the
      maximum.
  
  Examples
  --------
  >>> from sktime.regression.dummy import DummyRegressor
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train")
  >>> X_test, y_test = load_unit_test(split="test")
  >>> regressor = DummyRegressor(strategy="median") # doctest: +SKIP
  >>> regressor.fit(X_train,y_train) # doctest: +SKIP
  DummyRegressor(strategy='median')
  >>> y_pred = regressor.predict(X_test) # doctest: +SKIP
docstr: Fit the dummy regressor.
  
  Parameters
  ----------
  X : sktime-format pandas dataframe with shape(n,d),
  or numpy ndarray with shape(n,d,m)
  y : array-like, shape = [n_instances] - the target values
  
  Returns
  -------
  self : reference to self.
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code: def _fit(self, X, y) -> np.ndarray:
      """Fit the dummy regressor.
  
      Parameters
      ----------
      X : sktime-format pandas dataframe with shape(n,d),
      or numpy ndarray with shape(n,d,m)
      y : array-like, shape = [n_instances] - the target values
  
      Returns
      -------
      self : reference to self.
      """
      self.sklearn_dummy_regressor.fit(np.zeros(X.shape), y)
      return self
  
imports: import numpy
  from sklearn.dummy import DummyRegressor
  from sktime.regression.base import BaseRegressor
label_desc: Use image node_id 2 for calling the DummyRegressor obj's underlying member method code with example usage: obj._fit(X, y) and returns: self