old_contents: import tensorflow as tf
  
  
  class DQN(tf.keras.Model):
      def __init__(self, input_shape, num_actions):
          super().__init__()
          self.conv1 = tf.keras.layers.Conv2D(filters=16, kernel_size=(2, 2), activation="relu", input_shape=input_shape)
          self.flatten = tf.keras.layers.Flatten()
          self.q_values = tf.keras.layers.Dense(num_actions, activation="linear")
  
      def call(self, inputs):
          x = self.conv1(inputs)
          x = self.flatten(x)
          return self.q_values(x)
  
new_contents: import tensorflow as tf
  
  
  class MixtureOfConvolutions(tf.keras.layers.Layer):
      def __init__(self):
          super().__init__()
          self.conv21 = tf.keras.layers.Conv2D(filters=32, kernel_size=(2, 1), activation="relu", padding="same")
          self.conv12 = tf.keras.layers.Conv2D(filters=32, kernel_size=(1, 2), activation="relu", padding="same")
          self.conv31 = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 1), activation="relu", padding="same")
          self.conv13 = tf.keras.layers.Conv2D(filters=32, kernel_size=(1, 3), activation="relu", padding="same")
          self.conv41 = tf.keras.layers.Conv2D(filters=32, kernel_size=(4, 1), activation="relu", padding="same")
          self.conv14 = tf.keras.layers.Conv2D(filters=32, kernel_size=(1, 4), activation="relu", padding="same")
          self.conv32 = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 2), activation="relu", padding="same")
          self.conv23 = tf.keras.layers.Conv2D(filters=32, kernel_size=(2, 3), activation="relu", padding="same")
          self.conv22 = tf.keras.layers.Conv2D(filters=32, kernel_size=(2, 2), activation="relu", padding="same")
          self.conv33 = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu", padding="same")
  
      def call(self, inputs):
          x21 = self.conv21(inputs)
          x12 = self.conv12(inputs)
          x31 = self.conv31(inputs)
          x13 = self.conv13(inputs)
          x41 = self.conv41(inputs)
          x14 = self.conv14(inputs)
          x32 = self.conv32(inputs)
          x23 = self.conv23(inputs)
          x22 = self.conv22(inputs)
          x33 = self.conv33(inputs)
          x = tf.concat([x21, x12, x31, x13, x41, x14, x32, x23, x22, x33], axis=-1)
          return x
  
  
  class DQN(tf.keras.Model):
      def __init__(self, input_shape, num_actions):
          super().__init__()
          self.inp = tf.keras.layers.InputLayer(input_shape=input_shape)
          self.convmix1 = MixtureOfConvolutions()
          self.convmix2 = MixtureOfConvolutions()
          self.flatten = tf.keras.layers.Flatten()
          self.value_fc1 = tf.keras.layers.Dense(128, activation="relu")
          self.value_fc2 = tf.keras.layers.Dense(1, activation="linear")
          self.advantage_fc1 = tf.keras.layers.Dense(128, activation="relu")
          self.advantage_fc2 = tf.keras.layers.Dense(num_actions, activation="linear")
  
      def call(self, inputs):
          x = self.inp(inputs)
          x = self.convmix1(x)
          x = self.convmix2(x)
          x = self.flatten(x)
          value = self.value_fc1(x)
          value = self.value_fc2(value)
          advantage = self.advantage_fc1(x)
          advantage = self.advantage_fc2(advantage)
          q_values = value + (advantage - tf.reduce_mean(advantage, axis=1, keepdims=True))
          return q_values
  
subject: Make model dueling DQN with fancier architecture