code: def yolov3_base(input, num_anchors, num_classes):
      darknet_network = Model(input, darknet(input))
  
      network, network_1 = last_layers(
          darknet_network.output,
          512,
          num_anchors * (num_classes + 5),
          layer_name="last1",
      )
  
      network = NetworkConv2D_BN_Leaky(
          input=network, channels=256, kernel_size=(1, 1)
      )
      network = UpSampling2D(2)(network)
      network = Concatenate()(
          [network, darknet_network.layers[152].output]
      )
  
      network, network_2 = last_layers(
          network,
          256,
          num_anchors * (num_classes + 5),
          layer_name="last2",
      )
  
      network = NetworkConv2D_BN_Leaky(
          input=network, channels=128, kernel_size=(1, 1)
      )
      network = UpSampling2D(2)(network)
      network = Concatenate()(
          [network, darknet_network.layers[92].output]
      )
  
      network, network_3 = last_layers(
          network,
          128,
          num_anchors * (num_classes + 5),
          layer_name="last3",
      )
  
      return input, network_1, network_2, network_3
  
imports: from tensorflow.keras.layers import Conv2D, MaxPool2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, LeakyReLU, Lambda
  from tensorflow.keras.layers import LeakyReLU
  from tensorflow.keras.layers import BatchNormalization
  from tensorflow.keras.regularizers import l2
  from tensorflow.keras.models import Model
  from tensorflow.keras import Input
  from tensorflow.keras.layers import add, concatenate
  from tensorflow.keras.layers import Layer
  import tensorflow
label_desc: Use image node_id 6 for calling a global function with example usage: yolov3_base(input, num_anchors, num_classes) and returns: input,  network_1,  network_2,  network_3