class_docstr: Residual Neural Network as described in [1].
  
  Parameters
  ----------
  n_epochs       : int, default = 1500
      the number of epochs to train the model
  batch_size      : int, default = 16
      the number of samples per gradient update.
  random_state    : int or None, default=None
      Seed for random number generation.
  verbose         : boolean, default = False
      whether to output extra information
  loss            : string, default="mean_squared_error"
      fit parameter for the keras model
  optimizer       : keras.optimizer, default=keras.optimizers.Adam(),
  metrics         : list of strings, default=["accuracy"],
  activation      : string or a tf callable, default="sigmoid"
      Activation function used in the output linear layer.
      List of available activation functions:
      https://keras.io/api/layers/activations/
  use_bias        : boolean, default = True
      whether the layer uses a bias vector.
  optimizer       : keras.optimizers object, default = Adam(lr=0.01)
      specify the optimizer and the learning rate to be used.
  
  
  Notes
  -----
  Adapted from the implementation from source code
  https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py
  
  References
  ----------
      .. [1] Wang et. al, Time series classification from
  scratch with deep neural networks: A strong baseline,
  International joint conference on neural networks (IJCNN), 2017.
  
  Examples
  --------
  >>> from sktime.classification.deep_learning.resnet import ResNetClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train")
  >>> clf = ResNetClassifier(n_epochs=20, bacth_size=4) # doctest: +SKIP
  >>> clf.fit(X_train, Y_train) # doctest: +SKIP
  ResNetClassifier(...)
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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
docstr_tok: ["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()
      self.history = self.model_.fit(
          X,
          y_onehot,
          batch_size=self.batch_size,
          epochs=self.n_epochs,
          verbose=self.verbose,
          callbacks=deepcopy(self.callbacks) if self.callbacks else [],
      )
      return self
  
file_path: sktime/sktime/classification/deep_learning/resnet.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.resnet import ResNetNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 3 for calling the ResNetClassifier obj's underlying member method code with example usage: obj._fit(X, y) and returns: self