class_docstr: Multi Channel Deep Convolutional Neural Network (MCDCNN).
  
  Adapted from the implementation from Fawaz et. al:
  https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcdcnn.py
  
  Parameters
  ----------
  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.
  random_state : int, optional (default=0)
      The seed to any random action.
code: def __init__(
      self,
      kernel_size=5,
      pool_size=2,
      filter_sizes=(8, 8),
      dense_units=732,
      conv_padding="same",
      pool_padding="same",
      random_state=0,
  ):
      _check_dl_dependencies(severity="error")
      super().__init__()
  
      self.kernel_size = kernel_size
      self.pool_size = pool_size
      self.filter_sizes = filter_sizes
      self.dense_units = dense_units
      self.conv_padding = conv_padding
      self.pool_padding = pool_padding
      self.random_state = random_state
  
imports: from sktime.networks.base import BaseDeepNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 1 to create a new MCDCNNNetwork object from inherited base classes: BaseDeepNetwork with example: obj = MCDCNNNetwork(kernel_size, pool_size, filter_sizes, dense_units, conv_padding, pool_padding, random_state)