class_docstr: InceptionTime Deep Learning Classifier.
  
  Parameters
  ----------
  n_epochs : int, default=1500
  batch_size : int, default=64
      the number of samples per gradient update
  kernel_size : int, default=40
      specifying the length of the 1D convolution window
  n_filters : int, default=32
  use_residual : boolean, default=True
  use_bottleneck : boolean, default=True
  bottleneck_size : int, default=32
  depth : int, default=6
  callbacks : list of tf.keras.callbacks.Callback objects
  random_state: int, optional, default=None
      random seed for internal random number generator
  verbose: boolean, default=False
      whether to print runtime information
  loss: str, default="categorical_crossentropy"
  metrics: optional
  
  Notes
  -----
  ..[1] Fawaz et. al, InceptionTime: Finding AlexNet for Time Series
  Classification, Data Mining and Knowledge Discovery, 34, 2020
  
  Adapted from the implementation from Fawaz et. al
  https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
docstr: Fit the classifier on the training set (X, y).
  
  Parameters
  ----------
  X : np.ndarray of shape = (n_instances (n), n_dimensions (d), series_length (m))
      The training input samples.
  y : np.ndarray of shape n
      The training data class labels.
  
  Returns
  -------
  self : object
code: def _fit(self, X, y):
      """Fit the classifier on the training set (X, y).
  
      Parameters
      ----------
      X : np.ndarray of shape = (n_instances (n), n_dimensions (d), series_length (m))
          The training input samples.
      y : np.ndarray of shape n
          The training data class labels.
  
      Returns
      -------
      self : object
      """
      y_onehot = self.convert_y_to_keras(y)
      # Transpose to conform to Keras input style.
      X = X.transpose(0, 2, 1)
  
      check_random_state(self.random_state)
      self.input_shape = X.shape[1:]
      self.model_ = self.build_model(self.input_shape, self.n_classes_)
      if self.verbose:
          self.model_.summary()
  
      callbacks = self._check_callbacks(self.callbacks)
  
      self.history = self.model_.fit(
          X,
          y_onehot,
          batch_size=self.batch_size,
          epochs=self.n_epochs,
          verbose=self.verbose,
          callbacks=deepcopy(callbacks) if callbacks else [],
      )
      return self
  
file_path: sktime/sktime/classification/deep_learning/inceptiontime.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.inceptiontime import InceptionTimeNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 3 for calling the InceptionTimeClassifier obj's underlying member method code with example usage: obj._fit(X, y) and returns: self