code: def fprop(self, x, **kwargs):
      del kwargs
      my_conv = functools.partial(
          tf.layers.conv2d,
          kernel_size=3,
          strides=2,
          padding="valid",
          activation=tf.nn.relu,
          kernel_initializer=HeReLuNormalInitializer,
      )
      my_dense = functools.partial(
          tf.layers.dense, kernel_initializer=HeReLuNormalInitializer
      )
  
      with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
          for depth in [96, 256, 384, 384, 256]:
              x = my_conv(x, depth)
          y = tf.layers.flatten(x)
          y = my_dense(y, 4096, tf.nn.relu)
          y = fc7 = my_dense(y, 4096, tf.nn.relu)
          y = my_dense(y, 1000)
          return {
              "fc7": fc7,
              self.O_LOGITS: y,
              self.O_PROBS: tf.nn.softmax(logits=y),
          }
  
file_path: cleverhans/cleverhans_v3.1.0/examples/imagenet_featadvs/model.py
imports: import functools
  import tensorflow
  from cleverhans.initializers import HeReLuNormalInitializer
  from cleverhans.model import Model
label_desc: Use image node_id 2 for calling the ModelImageNetCNN obj's underlying member method code with example usage: obj.fprop(x) and returns: dict