class_docstr: Multi Channel Deep Convolutional Neural Regressor, adopted from [1]_.
  
  Parameters
  ----------
  n_epochs : int, optional (default=120)
      The number of epochs to train the model.
  batch_size : int, optional (default=16)
      The number of samples per gradient update.
  kernel_size : int, optional (default=5)
      The size of kernel in Conv1D layer.
  pool_size : int, optional (default=2)
      The size of kernel in (Max) Pool layer.
  filter_sizes : tuple, optional (default=(8, 8))
      The sizes of filter for Conv1D layer corresponding
      to each Conv1D in the block.
  dense_units : int, optional (default=732)
      The number of output units of the final Dense
      layer of this Network. This is NOT the final layer
      but the penultimate layer.
  conv_padding : str or None, optional (default="same")
      The type of padding to be applied to convolutional
      layers.
  pool_padding : str or None, optional (default="same")
      The type of padding to be applied to pooling layers.
  loss : str, optional (default="mean_squared_error")
      The name of the loss function to be used during training,
      should be supported by keras.
  activation : str, optional (default="linear")
      The activation function to apply at the output.
  use_bias : bool, optional (default=True)
      Whether bias should be included in the output layer.
  metrics : None or string, optional (default=None)
      The string which will be used during model compilation. If left as None,
      then "mean_squared_error" is passed to `model.compile()`.
  optimizer: None or keras.optimizers.Optimizer instance, optional (default=None)
      The optimizer that is used for model compiltation. If left as None,
      then `keras.optimizers.SGD` is used with the following parameters -
      `learning_rate=0.01, momentum=0.9, weight_decay=0.0005`.
  callbacks : None or list of keras.callbacks.Callback, optional (default=None)
      The callback(s) to use during training.
  random_state : int, optional (default=0)
      The seed to any random action.
  
  Notes
  -----
  Adapted from the implementation of Fawaz et. al
  https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcdcnn.py
  
  References
  ----------
  .. [1] Zheng et. al, Time series classification using multi-channels deep
    convolutional neural networks, International Conference on
    Web-Age Information Management, Pages 298-310, year 2014, organization: Springer.
  
  Examples
  --------
  >>> from sktime.regression.deep_learning.mcdcnn import MCDCNNRegressor
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train")
  >>> mcdcnn = MCDCNNRegressor(n_epochs=1, kernel_size=4) # doctest: +SKIP
  >>> mcdcnn.fit(X_train, y_train) # doctest: +SKIP
  MCDCNRegressor(...)
docstr: Fit the regressor 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 regressor 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
      """
      X = X.transpose(0, 2, 1)
      self.input_shape = X.shape[1:]
      X = self._network._prepare_input(X)
  
      check_random_state(self.random_state)
  
      self.model_ = self.build_model(self.input_shape)
      self.callbacks_ = deepcopy(self.callbacks)
  
      if self.verbose:
          self.model_.summary()
  
      self.history = self.model_.fit(
          X,
          y,
          batch_size=self.batch_size,
          verbose=self.verbose,
          callbacks=self.callbacks_,
      )
  
      return self
  
file_path: sktime/sktime/regression/deep_learning/mcdcnn.py
imports: from copy import deepcopy
  from numpy import squeeze
  from sklearn.utils import check_random_state
  from sktime.networks.mcdcnn import MCDCNNNetwork
  from sktime.regression.deep_learning.base import BaseDeepRegressor
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 3 for calling the MCDCNNRegressor obj's underlying member method code with example usage: obj._fit(X, y) and returns: self