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.
docstr: Construct a network and return its input and output layers.
  
  Parameters
  ----------
  input_shape : tuple
      The shape of the data(without batch) fed into the input layer
  
  Returns
  -------
  input_layer : An instance of keras.layers.Input
      The input layer of this Network.
  output_layer: An instance of keras.layers.Layer
      The output layer of this Network.
docstr_tok: ["Construct","a","network","and","return","its","input","and","output","layers",".","Parameters","--","--","--","--","--","input_shape",":","tuple","The","shape","of","the","data","(","without","batch",")","fed","into","the","input","layer","Returns","--","--","--","-","input_layer",":","An","instance","of","keras.layers.Input","The","input","layer","of","this","Network",".","output_layer",":","An","instance","of","keras.layers.Layer","The","output","layer","of","this","Network","."]
code: def build_network(self, input_shape, **kwargs):
      """Construct a network and return its input and output layers.
  
      Parameters
      ----------
      input_shape : tuple
          The shape of the data(without batch) fed into the input layer
  
      Returns
      -------
      input_layer : An instance of keras.layers.Input
          The input layer of this Network.
      output_layer: An instance of keras.layers.Layer
          The output layer of this Network.
      """
      from tensorflow import keras, reduce_mean
  
      input_layer = keras.layers.Input(shape=input_shape)
  
      x = self._stack(
          input_layer,
          self.repeats,
          self.filter_sizes[0],
          self.reduction,
      )
      x = keras.layers.MaxPooling1D(
          self.pool_size, self.strides, padding=self.padding
      )(x)
  
      x = self._stack(
          x, self.repeats, self.filter_sizes[1], self.reduction
      )
      x = keras.layers.MaxPooling1D(
          self.pool_size, self.strides, padding=self.padding
      )(x)
  
      x = self._stack(
          x, self.repeats, self.filter_sizes[2], self.reduction
      )
  
      output_layer = reduce_mean(x, 1)
  
      return input_layer, output_layer
  
imports: from sktime.networks.base import BaseDeepNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 4 for calling the MACNNNetwork obj's underlying member method code with example usage: obj.build_network(input_shape) and returns: input_layer,  output_layer