code: def shape_weights_hidden(self) -> Tuple[int, int, int]:
          
          if self.nmb_layers > 1:
              nmb_neurons = self.nmb_neurons
              return (self.nmb_layers-1,
                      max(nmb_neurons[:-1]),
                      max(nmb_neurons[1:]))
          return 0, 0, 0
docs: Shape of the array containing the activation of the hidden neurons.
  
          The first integer value is the number of connection between the
          hidden layers, the second integer value is maximum number of
          neurons of all hidden layers feeding information into another
          hidden layer (all except the last one), and the third integer
          value is the maximum number of the neurons of all hidden layers
          receiving information from another hidden layer (all except the
          first one):
  
          >>> from hydpy import ANN
          >>> ann = ANN(None)
          >>> ann(nmb_inputs=6, nmb_neurons=(4, 3, 2), nmb_outputs=6)
          >>> ann.shape_weights_hidden
          (2, 4, 3)
          >>> ann(nmb_inputs=6, nmb_neurons=(4,), nmb_outputs=6)
          >>> ann.shape_weights_hidden
          (0, 0, 0)