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: Build MACNN Blocks and stack them.
  
  Parameters
  ----------
  x : Instance of keras.layers.Layer
      The previous layer, in case of the first
      block it represents the input layer.
  repeats : int
      The number of MACNN Blocks to be used.
      Corresponds to `repeats` attribute.
  kernels : int
      The base output dimension for dense layers, it corresponds
      to elements `filter_sizes` attributes.
  
  Returns
  -------
  x : Instance of keras.layers.Layer
      The final layer after repeatedly applying MACNN Blocks.
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code: def _stack(self, x, repeats, kernels, reduce):
      """Build MACNN Blocks and stack them.
  
      Parameters
      ----------
      x : Instance of keras.layers.Layer
          The previous layer, in case of the first
          block it represents the input layer.
      repeats : int
          The number of MACNN Blocks to be used.
          Corresponds to `repeats` attribute.
      kernels : int
          The base output dimension for dense layers, it corresponds
          to elements `filter_sizes` attributes.
  
      Returns
      -------
      x : Instance of keras.layers.Layer
          The final layer after repeatedly applying MACNN Blocks.
      """
      for _ in range(repeats):
          x = self._macnn_block(x, kernels, reduce)
      return x
  
imports: from sktime.networks.base import BaseDeepNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 3 for calling the MACNNNetwork obj's underlying member method code with example usage: obj._stack(x, repeats, kernels, reduce) and returns: x