class_docstr: DummyClassifier makes predictions that ignore the input features.
  
  This classifier serves as a simple baseline to compare against other more
  complex classifiers.
  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.DummyClassifier`, which is called inside.
  
  Parameters
  ----------
  strategy : {"most_frequent", "prior", "stratified", "uniform",             "constant"}, default="prior"
      Strategy to use to generate predictions.
      * "most_frequent": the `predict` method always returns the most
        frequent class label in the observed `y` argument passed to `fit`.
        The `predict_proba` method returns the matching one-hot encoded
        vector.
      * "prior": the `predict` method always returns the most frequent
        class label in the observed `y` argument passed to `fit` (like
        "most_frequent"). ``predict_proba`` always returns the empirical
        class distribution of `y` also known as the empirical class prior
        distribution.
      * "stratified": the `predict_proba` method randomly samples one-hot
        vectors from a multinomial distribution parametrized by the empirical
        class prior probabilities.
        The `predict` method returns the class label which got probability
        one in the one-hot vector of `predict_proba`.
        Each sampled row of both methods is therefore independent and
        identically distributed.
      * "uniform": generates predictions uniformly at random from the list
        of unique classes observed in `y`, i.e. each class has equal
        probability.
      * "constant": always predicts a constant label that is provided by
        the user. This is useful for metrics that evaluate a non-majority
        class.
  random_state : int, RandomState instance or None, default=None
      Controls the randomness to generate the predictions when
      ``strategy='stratified'`` or ``strategy='uniform'``.
      Pass an int for reproducible output across multiple function calls.
      See :term:`Glossary <random_state>`.
  constant : int or str 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.
docstr: Predict labels for sequences in X.
  
  Parameters
  ----------
  X : sktime-format pandas dataframe or array-like, shape (n, d)
  
  Returns
  -------
  y : predictions of labels for X, np.ndarray
code: def _predict(self, X) -> np.ndarray:
      """Predict labels for sequences in X.
  
      Parameters
      ----------
      X : sktime-format pandas dataframe or array-like, shape (n, d)
  
      Returns
      -------
      y : predictions of labels for X, np.ndarray
      """
      return self.sklearn_dummy_classifier.predict(np.zeros(X.shape))
  
file_path: sktime/sktime/classification/dummy/_dummy.py
imports: import numpy
  from sklearn.dummy import DummyClassifier
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 3 for calling the DummyClassifier obj's underlying member method code with example usage: obj._predict(X) and returns: self