class_docstr: Multi Channel Deep Convolutional Neural Classifier, as described in [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="categorical_crossentropy")
      The name of the loss function to be used during training,
      should be supported by keras.
  activation : str, optional (default="sigmoid")
      The activation function to apply at the output. It should be
      "software" if response variable has more than two types.
  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 "accuracy" 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.classification.deep_learning.mcdcnn import MCDCNNClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_tain = load_unit_test(split="train")
  >>> mcdcnn = MCDCNNClassifier()     # doctest: +SKIP
  >>> mcdcnn.fit(X_train, y_train)    # doctest: +SKIP
  MCDCNNClassifier(...)
docstr: Find probability estimates for each class for all cases in X.
  
  Parameters
  ----------
  X : an np.ndarray of shape = (n_instances, n_dimensions, series_length)
      The training input samples.
  
  Returns
  -------
  output : array of shape = [n_instances, n_classes] of probabilities
code: def _predict_proba(self, X, **kwargs):
      """Find probability estimates for each class for all cases in X.
  
      Parameters
      ----------
      X : an np.ndarray of shape = (n_instances, n_dimensions, series_length)
          The training input samples.
  
      Returns
      -------
      output : array of shape = [n_instances, n_classes] of probabilities
      """
      self.check_is_fitted()
      X = X.transpose([0, 2, 1])
      X = self._network._prepare_input(X)
  
      probs = self.model_.predict(X, self.batch_size, **kwargs)
  
      if probs.shape[1] == 1:
          probs = np.hstack([1 - probs, probs])
      probs = probs / probs.sum(axis=1, keepdims=1)
      return probs
  
file_path: sktime/sktime/classification/deep_learning/mcdcnn.py
imports: from copy import deepcopy
  import numpy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.mcdcnn import MCDCNNNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 4 for calling the MCDCNNClassifier obj's underlying member method code with example usage: obj._predict_proba(X) and returns: probs