code: def alexnet(input, class_dim):
      conv1 = paddle.layer.img_conv(
          input=input,
          filter_size=11,
          num_channels=3,
          num_filters=96,
          stride=4,
          padding=1,
      )
      cmrnorm1 = paddle.layer.img_cmrnorm(
          input=conv1, size=5, scale=0.0001, power=0.75
      )
      pool1 = paddle.layer.img_pool(
          input=cmrnorm1, pool_size=3, stride=2
      )
  
      conv2 = paddle.layer.img_conv(
          input=pool1,
          filter_size=5,
          num_filters=256,
          stride=1,
          padding=2,
          groups=1,
      )
      cmrnorm2 = paddle.layer.img_cmrnorm(
          input=conv2, size=5, scale=0.0001, power=0.75
      )
      pool2 = paddle.layer.img_pool(
          input=cmrnorm2, pool_size=3, stride=2
      )
  
      pool3 = paddle.networks.img_conv_group(
          input=pool2,
          pool_size=3,
          pool_stride=2,
          conv_num_filter=[384, 384, 256],
          conv_filter_size=3,
          pool_type=paddle.pooling.Max(),
      )
  
      fc1 = paddle.layer.fc(
          input=pool3,
          size=4096,
          act=paddle.activation.Relu(),
          layer_attr=paddle.attr.Extra(drop_rate=0.5),
      )
      fc2 = paddle.layer.fc(
          input=fc1,
          size=4096,
          act=paddle.activation.Relu(),
          layer_attr=paddle.attr.Extra(drop_rate=0.5),
      )
  
      out = paddle.layer.fc(
          input=fc2, size=class_dim, act=paddle.activation.Softmax()
      )
      return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: alexnet(input, class_dim) and returns: out