code: def build_rnet(self, input_shape=None):
      if input_shape is None:
          input_shape = (24, 24, 3)
  
      r_inp = Input(input_shape)
  
      r_layer = Conv2D(
          28, kernel_size=(3, 3), strides=(1, 1), padding="valid"
      )(r_inp)
      r_layer = PReLU(shared_axes=[1, 2])(r_layer)
      r_layer = MaxPooling2D(
          pool_size=(3, 3), strides=(2, 2), padding="same"
      )(r_layer)
  
      r_layer = Conv2D(
          48, kernel_size=(3, 3), strides=(1, 1), padding="valid"
      )(r_layer)
      r_layer = PReLU(shared_axes=[1, 2])(r_layer)
      r_layer = MaxPooling2D(
          pool_size=(3, 3), strides=(2, 2), padding="valid"
      )(r_layer)
  
      r_layer = Conv2D(
          64, kernel_size=(2, 2), strides=(1, 1), padding="valid"
      )(r_layer)
      r_layer = PReLU(shared_axes=[1, 2])(r_layer)
      r_layer = Flatten()(r_layer)
      r_layer = Dense(128)(r_layer)
      r_layer = PReLU()(r_layer)
  
      r_layer_out1 = Dense(2)(r_layer)
      r_layer_out1 = Softmax(axis=1)(r_layer_out1)
  
      r_layer_out2 = Dense(4)(r_layer)
  
      r_net = Model(r_inp, [r_layer_out2, r_layer_out1])
  
      return r_net
  
imports: from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, PReLU, Flatten, Softmax
  from tensorflow.keras.models import Model
  import numpy
label_desc: Use image node_id 2 for calling the NetworkFactory obj's underlying member method code with example usage: obj.build_rnet(input_shape) and returns: r_net