class_docstr: Random Interval Classifier.
  
  This classifier simply transforms the input data using the RandomIntervals
  transformer and builds a provided estimator using the transformed data.
  
  Parameters
  ----------
  n_intervals : int, default=100,
      The number of intervals of random length, position and dimension to be
      extracted.
  interval_transformers : transformer or list of transformers, default=None,
      Transformer(s) used to extract features from each interval. If None, defaults to
      the Catch22 transformer.
  estimator : sklearn classifier, default=None
      An sklearn estimator to be built using the transformed data. Defaults to a
      Rotation Forest with 200 trees.
  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, integer.
  
  Attributes
  ----------
  n_classes_ : int
      Number of classes. Extracted from the data.
  classes_ : ndarray of shape (n_classes)
      Holds the label for each class.
  
  See Also
  --------
  RandomIntervals
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docstr: Fit a pipeline on cases (X,y), where y is the target variable.
  
  Parameters
  ----------
  X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
      The training data.
  y : array-like, shape = [n_instances]
      The class labels.
  
  Returns
  -------
  self :
      Reference to self.
  
  Notes
  -----
  Changes state by creating a fitted model that updates attributes
  ending in "_" and sets is_fitted flag to True.
docstr_tok: ["Fit","a","pipeline","on","cases","(","X",",","y",")",",","where","y","is","the","target","variable",".","Parameters","--","--","--","--","--","X",":","3D","np.array","of","shape","=","[","n_instances",",","n_dimensions",",","series_length","]","The","training","data",".","y",":","array-like",",","shape","=","[","n_instances","]","The","class","labels",".","Returns","--","--","--","-","self",":","Reference","to","self",".","Notes","--","--","-","Changes","state","by","creating","a","fitted","model","that","updates","attributes","ending","in","``","_","''","and","sets","is_fitted","flag","to","True","."]
code: def _fit(self, X, y):
      """Fit a pipeline on cases (X,y), where y is the target variable.
  
      Parameters
      ----------
      X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
          The training data.
      y : array-like, shape = [n_instances]
          The class labels.
  
      Returns
      -------
      self :
          Reference to self.
  
      Notes
      -----
      Changes state by creating a fitted model that updates attributes
      ending in "_" and sets is_fitted flag to True.
      """
      interval_transformers = (
          Catch22(outlier_norm=True, replace_nans=True)
          if self.interval_transformers is None
          else self.interval_transformers
      )
  
      self._transformer = RandomIntervals(
          n_intervals=self.n_intervals,
          transformers=interval_transformers,
          random_state=self.random_state,
          n_jobs=self._threads_to_use,
      )
  
      self._estimator = _clone_estimator(
          RotationForest()
          if self.estimator is None
          else self.estimator,
          self.random_state,
      )
  
      m = getattr(self._estimator, "n_jobs", None)
      if m is not None:
          self._estimator.n_jobs = self._threads_to_use
  
      X_t = self._transformer.fit_transform(X, y)
      self._estimator.fit(X_t, y)
  
      return self
  
file_path: sktime/sktime/classification/feature_based/_random_interval_classifier.py
imports: import numpy
  from sktime.base._base import _clone_estimator
  from sktime.classification.base import BaseClassifier
  from sktime.classification.sklearn import RotationForest
  from sktime.transformations.panel.catch22 import Catch22
  from sktime.transformations.panel.random_intervals import RandomIntervals
label_desc: Use image node_id 2 for calling the RandomIntervalClassifier obj's underlying member method code with example usage: obj._fit(X, y) and returns: self