code: def get_symbol(num_classes=1000, **kwargs):
      data = mx.symbol.Variable(name="data")
      conv1a_3_3 = ConvFactory(
          data=data, num_filter=32, kernel=(3, 3), stride=(2, 2)
      )
      conv2a_3_3 = ConvFactory(conv1a_3_3, 32, (3, 3))
      conv2b_3_3 = ConvFactory(conv2a_3_3, 64, (3, 3), pad=(1, 1))
      maxpool3a_3_3 = mx.symbol.Pooling(
          data=conv2b_3_3, kernel=(3, 3), stride=(2, 2), pool_type="max"
      )
      conv3b_1_1 = ConvFactory(maxpool3a_3_3, 80, (1, 1))
      conv4a_3_3 = ConvFactory(conv3b_1_1, 192, (3, 3))
      maxpool5a_3_3 = mx.symbol.Pooling(
          data=conv4a_3_3, kernel=(3, 3), stride=(2, 2), pool_type="max"
      )
  
      tower_conv = ConvFactory(maxpool5a_3_3, 96, (1, 1))
      tower_conv1_0 = ConvFactory(maxpool5a_3_3, 48, (1, 1))
      tower_conv1_1 = ConvFactory(tower_conv1_0, 64, (5, 5), pad=(2, 2))
  
      tower_conv2_0 = ConvFactory(maxpool5a_3_3, 64, (1, 1))
      tower_conv2_1 = ConvFactory(tower_conv2_0, 96, (3, 3), pad=(1, 1))
      tower_conv2_2 = ConvFactory(tower_conv2_1, 96, (3, 3), pad=(1, 1))
  
      tower_pool3_0 = mx.symbol.Pooling(
          data=maxpool5a_3_3,
          kernel=(3, 3),
          stride=(1, 1),
          pad=(1, 1),
          pool_type="avg",
      )
      tower_conv3_1 = ConvFactory(tower_pool3_0, 64, (1, 1))
      tower_5b_out = mx.symbol.Concat(
          *[tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1]
      )
      net = repeat(
          tower_5b_out, 10, block35, scale=0.17, input_num_channels=320
      )
      tower_conv = ConvFactory(net, 384, (3, 3), stride=(2, 2))
      tower_conv1_0 = ConvFactory(net, 256, (1, 1))
      tower_conv1_1 = ConvFactory(
          tower_conv1_0, 256, (3, 3), pad=(1, 1)
      )
      tower_conv1_2 = ConvFactory(
          tower_conv1_1, 384, (3, 3), stride=(2, 2)
      )
      tower_pool = mx.symbol.Pooling(
          net, kernel=(3, 3), stride=(2, 2), pool_type="max"
      )
      net = mx.symbol.Concat(*[tower_conv, tower_conv1_2, tower_pool])
      net = repeat(net, 20, block17, scale=0.1, input_num_channels=1088)
      tower_conv = ConvFactory(net, 256, (1, 1))
      tower_conv0_1 = ConvFactory(
          tower_conv, 384, (3, 3), stride=(2, 2)
      )
      tower_conv1 = ConvFactory(net, 256, (1, 1))
      tower_conv1_1 = ConvFactory(
          tower_conv1, 288, (3, 3), stride=(2, 2)
      )
      tower_conv2 = ConvFactory(net, 256, (1, 1))
      tower_conv2_1 = ConvFactory(tower_conv2, 288, (3, 3), pad=(1, 1))
      tower_conv2_2 = ConvFactory(
          tower_conv2_1, 320, (3, 3), stride=(2, 2)
      )
      tower_pool = mx.symbol.Pooling(
          net, kernel=(3, 3), stride=(2, 2), pool_type="max"
      )
      net = mx.symbol.Concat(
          *[tower_conv0_1, tower_conv1_1, tower_conv2_2, tower_pool]
      )
  
      net = repeat(net, 9, block8, scale=0.2, input_num_channels=2080)
      net = block8(net, with_act=False, input_num_channels=2080)
  
      net = ConvFactory(net, 1536, (1, 1))
      net = mx.symbol.Pooling(
          net,
          kernel=(1, 1),
          global_pool=True,
          stride=(2, 2),
          pool_type="avg",
      )
      net = mx.symbol.Flatten(net)
      net = mx.symbol.Dropout(data=net, p=0.2)
      net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes)
      softmax = mx.symbol.SoftmaxOutput(data=net, name="softmax")
      return softmax
  
imports: import mxnet
label_desc: Use image node_id 6 for calling a global function with example usage: get_symbol(num_classes) and returns: softmax