code: def model(
      img_in,
      num_actions,
      scope,
      noisy=False,
      reuse=False,
      concat_softmax=False,
  ):
      with tf.variable_scope(scope, reuse=reuse):
          out = img_in
          with tf.variable_scope("convnet"):
              # original architecture
              out = layers.convolution2d(
                  out,
                  num_outputs=32,
                  kernel_size=8,
                  stride=4,
                  activation_fn=tf.nn.relu,
              )
              out = layers.convolution2d(
                  out,
                  num_outputs=64,
                  kernel_size=4,
                  stride=2,
                  activation_fn=tf.nn.relu,
              )
              out = layers.convolution2d(
                  out,
                  num_outputs=64,
                  kernel_size=3,
                  stride=1,
                  activation_fn=tf.nn.relu,
              )
          out = layers.flatten(out)
  
          with tf.variable_scope("action_value"):
              if noisy:
                  # Apply noisy network on fully connected layers
                  # ref: https://arxiv.org/abs/1706.10295
                  out = noisy_dense(
                      out,
                      name="noisy_fc1",
                      size=512,
                      activation_fn=tf.nn.relu,
                  )
                  out = noisy_dense(
                      out, name="noisy_fc2", size=num_actions
                  )
              else:
                  out = layers.fully_connected(
                      out, num_outputs=512, activation_fn=tf.nn.relu
                  )
                  out = layers.fully_connected(
                      out, num_outputs=num_actions, activation_fn=None
                  )
              # V: Softmax - inspired by deep-rl-attack #
              if concat_softmax:
                  out = tf.nn.softmax(out)
          return out
  
imports: import tensorflow
  import tensorflow.contrib.layers
  from rlattack.common.tf_util import noisy_dense
label_desc: Use image node_id 1 for calling a global function with example usage: model(img_in, num_actions, scope, noisy, reuse, concat_softmax) and returns: out