code: def vgg16(input, num_class):
      x = tflearn.conv_2d(
          input, 64, 3, activation="relu", scope="conv1_1"
      )
      x = tflearn.conv_2d(x, 64, 3, activation="relu", scope="conv1_2")
      x = tflearn.max_pool_2d(x, 2, strides=2, name="maxpool1")
  
      x = tflearn.conv_2d(x, 128, 3, activation="relu", scope="conv2_1")
      x = tflearn.conv_2d(x, 128, 3, activation="relu", scope="conv2_2")
      x = tflearn.max_pool_2d(x, 2, strides=2, name="maxpool2")
  
      x = tflearn.conv_2d(x, 256, 3, activation="relu", scope="conv3_1")
      x = tflearn.conv_2d(x, 256, 3, activation="relu", scope="conv3_2")
      x = tflearn.conv_2d(x, 256, 3, activation="relu", scope="conv3_3")
      x = tflearn.max_pool_2d(x, 2, strides=2, name="maxpool3")
  
      x = tflearn.conv_2d(x, 512, 3, activation="relu", scope="conv4_1")
      x = tflearn.conv_2d(x, 512, 3, activation="relu", scope="conv4_2")
      x = tflearn.conv_2d(x, 512, 3, activation="relu", scope="conv4_3")
      x = tflearn.max_pool_2d(x, 2, strides=2, name="maxpool4")
  
      x = tflearn.conv_2d(x, 512, 3, activation="relu", scope="conv5_1")
      x = tflearn.conv_2d(x, 512, 3, activation="relu", scope="conv5_2")
      x = tflearn.conv_2d(x, 512, 3, activation="relu", scope="conv5_3")
      x = tflearn.max_pool_2d(x, 2, strides=2, name="maxpool5")
  
      x = tflearn.fully_connected(
          x, 4096, activation="relu", scope="fc6"
      )
      x = tflearn.dropout(x, 0.5, name="dropout1")
  
      x = tflearn.fully_connected(
          x, 4096, activation="relu", scope="fc7"
      )
      x = tflearn.dropout(x, 0.5, name="dropout2")
  
      x = tflearn.fully_connected(
          x, num_class, activation="softmax", scope="fc8", restore=False
      )
  
      return x
  
imports: import tflearn
  from tflearn.data_preprocessing import ImagePreprocessing
  import os
  from tflearn.data_utils import image_preloader
label_desc: Use image node_id 1 for calling a global function with example usage: vgg16(input, num_class) and returns: x