class_docstr: Classifier wrapped for the Rocket transformer using RidgeClassifierCV.
  
  This classifier simply transforms the input data using the Rocket [1]_
  transformer and builds a RidgeClassifierCV estimator using the transformed data.
  
  Shorthand for the pipeline
  `rocket * StandardScaler(with_mean=False) * RidgeClassifierCV(alphas)`
  where `alphas = np.logspace(-3, 3, 10)`, and
  where `rocket` depends on params `rocket_transform`, `use_multivariate` as follows
  
  ================ ================== =======================
  rocket_transform `use_multivariate` rocket (class)
  ================ ================== =======================
  "rocket"         any                Rocket
  "minirocket"     "yes"               MiniRocketMultivariate
  "minirocket"     "no"               MiniRocket
  "multirocket"    "yes"              MultiRocketMultivariate
  "multirocket"    "no"               MultiRocket
  ================ ================== =======================
  
  classes are sktime classes, other parameters are passed on to the rocket class.
  
  To build other classifiers with rocket transformers, use `make_pipeline` or the
  pipeline dunder `*`, and different transformers/classifiers in combination.
  
  Parameters
  ----------
  num_kernels : int, optional, default=10,000
      The number of kernels for the Rocket transform.
  rocket_transform : str, optional, default="rocket"
      The type of Rocket transformer to use.
      Valid inputs = ["rocket", "minirocket", "multirocket"]
  max_dilations_per_kernel : int, optional, default=32
      MiniRocket and MultiRocket only. The maximum number of dilations per kernel.
  n_features_per_kernel : int, optional, default=4
      MultiRocket only. The number of features per kernel.
  use_multivariate : str, ["auto", "yes", "no"], optional, default="auto"
      whether to use multivariate rocket transforms or univariate ones
      "auto" = multivariate iff data seen in fit is multivariate, otherwise univariate
      "yes" = always uses multivariate transformers, native multi/univariate
      "no" = always univariate transformers, multivariate by framework vectorization
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int or None, default=None
      Seed for random number generation.
  
  Attributes
  ----------
  n_classes : int
      The number of classes.
  classes_ : list
      The classes labels.
  estimator_ : ClassifierPipeline
      RocketClassifier as a ClassifierPipeline, fitted to data internally
  
  See Also
  --------
  Rocket
  
  Notes
  -----
  For the Java version, see
  `TSML <https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/
  tsml/classifiers/shapelet_based/ROCKETClassifier.java>`_.
  
  References
  ----------
  .. [1] Dempster, Angus, François Petitjean, and Geoffrey I. Webb. "Rocket:
     exceptionally fast and accurate time series classification using random
     convolutional kernels." Data Mining and Knowledge Discovery 34.5 (2020)
  
  Examples
  --------
  >>> from sktime.classification.kernel_based import RocketClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
  >>> X_test, y_test = load_unit_test(split="test", return_X_y=True) # doctest: +SKIP
  >>> clf = RocketClassifier(num_kernels=500) # doctest: +SKIP
  >>> clf.fit(X_train, y_train) # doctest: +SKIP
  RocketClassifier(...)
  >>> y_pred = clf.predict(X_test) # doctest: +SKIP
code: def _get_delegate(self):
      use_multivariate = self.use_multivariate
      if use_multivariate == "auto":
          code_dict = {True: "yes", False: "no"}
          use_multivariate = code_dict[
              not self._X_metadata["is_univariate"]
          ]
  
      if use_multivariate == "yes":
          delegate = self.multivar_rocket_
      else:
          delegate = self.univar_rocket_
  
      return delegate
  
file_path: sktime/sktime/classification/kernel_based/_rocket_classifier.py
imports: import numpy
  from sklearn.linear_model import RidgeClassifierCV
  from sklearn.preprocessing import StandardScaler
  from sktime.classification._delegate import _DelegatedClassifier
  from sktime.pipeline import make_pipeline
  from sktime.transformations.panel.rocket import MiniRocket, MiniRocketMultivariate, MultiRocket, MultiRocketMultivariate, Rocket
label_desc: Use image node_id 3 for calling the RocketClassifier obj's underlying member method code with example usage: obj._get_delegate() and returns: delegate