class_docstr: Base MACNN Network for MACNNClassifier and MACNNRegressor.
  
  Parameters
  ----------
  padding : str, optional (default="same")
      The type of padding to be provided in MACNN Blocks. Accepts
      all the string values that keras.layers supports.
  pool_size : int, optional (default=3)
      A single value representing pooling windows which are applied
      between two MACNN Blocks.
  strides : int, optional (default=2)
      A single value representing strides to be taken during the
      pooling operation.
  repeats : int, optional (default=2)
      The number of MACNN Blocks to be stacked.
  filter_sizes : tuple, optional (default=(64, 128, 256))
      The input size of Conv1D layers within each MACNN Block.
  kernel_size : tuple, optional (default=(3, 6, 12))
      The output size of Conv1D layers within each MACNN Block.
  reduction : int, optional (default=16)
      The factor by which the first dense layer of a MACNN Block will be divided by.
  random_state: int, optional (default=0)
      The seed to any random action.
code: def __init__(
      self,
      padding="same",
      pool_size=3,
      strides=2,
      repeats=2,
      filter_sizes=(64, 128, 256),
      kernel_size=(3, 6, 12),
      reduction=16,
      random_state=0,
  ):
      _check_dl_dependencies(severity="error")
      super().__init__()
  
      self.padding = padding
      self.pool_size = pool_size
      self.strides = strides
      self.repeats = repeats
      self.filter_sizes = filter_sizes
      self.kernel_size = kernel_size
      self.reduction = reduction
      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 MACNNNetwork object from inherited base classes: BaseDeepNetwork with example: obj = MACNNNetwork(padding, pool_size, strides, repeats, filter_sizes, kernel_size, reduction, random_state)