class CustomActorNetwork(network.Network):
        def __init__(self, input_tensor_spec, output_tensor_spec, name='CustomActorNetwork'):
            super(CustomActorNetwork, self).__init__(
                input_tensor_spec=input_tensor_spec,
                state_spec=(),
                name=name)
            
            self.conv1 = Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), activation='relu')
            self.pool1 = MaxPooling2D((2, 2))
            self.conv2 = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu')
            self.pool2 = MaxPooling2D((2, 2))
            self.conv3 = Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1), activation='relu')
            self.pool3 = MaxPooling2D((2, 2))
            self.reshape = Reshape((-1, 128))
            self.lstm = LSTM(512, return_sequences=False, use_bias=True)
            self.batch_norm = BatchNormalization()
            self.dense1 = [Dense(128, activation='relu') for _ in range(4)]
            self.dense2 = [Dense(64, activation='relu') for _ in range(4)]
            self.out1 = Dense(3, activation='softmax')
            self.out2 = Dense(100, activation='softmax')
            self.out3 = Dense(100, activation='softmax')
            self.out4 = Dense(30, activation='softmax')
    
        def call(self, observation, step_type=None, network_state=(), training=False):
            x = self.conv1(observation)
            x = self.pool1(x)
            x = self.conv2(x)
            x = self.pool2(x)
            x = self.conv3(x)
            x = self.pool3(x)
            x = self.reshape(x)
            x = self.lstm(x)
            x = self.batch_norm(x)
            x_out1 = self.dense1[0](x)
            x_out1 = self.dense2[0](x_out1)
            out1_logits = self.position(x_out1)
            out1_dist = tfp.distributions.Categorical(logits=out1_logits).sample()
            xc = concatenate([out1_logits, x], axis=-1)
            x_out2 = self.dense1[1](xc)
            x_out2 = self.dense2[1](x_out2)
            out2_logits = self.up_band(x_out2)
            out2_dist = tfp.distributions.Categorical(logits=out2_logits).sample()
            x_out3 = self.dense1[2](xc)
            x_out3 = self.dense2[2](x_out3)
            out3_logits = self.down_band(x_out3)
            out3_dist = tfp.distributions.Categorical(logits=out3_logits).sample()
            xc2 = concatenate([xc, out2_logits, out3_logits], axis=-1)
            x_out4 = self.dense1[3](xc2)
            x_out4 = self.dense2[3](x_out4)
            out4_logits = self.volume(x_out4)
            out4_dist = tfp.distributions.Categorical(logits=out4_logits).sample()
            return tf.stack([out1_dist, out2_dist, out3_dist, out4_dist], axis=-1), network_state        

    array_spec.BoundedArraySpec(
                shape=(4,), dtype=np.int32, minimum=[0, 0, 0, 0], maximum=[2, 99, 99, 29], name='action')

    actor_net = Model.CustomActorNetwork(observation_spec,action_spec)
    value_net = Model.CustomValueNetwork(observation_spec,action_spec)
    
    optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-4)
    train_step_counter = tf.Variable(0)
  
    tf_agent = ppo_agent.PPOAgent(
        time_step_spec=train_env.time_step_spec(),
        action_spec=action_spec,
        optimizer=optimizer,
        actor_net=actor_net,
        value_net=value_net,
        num_epochs=5,
        train_step_counter=train_step_counter
    )

    Exception has occurred: ValueError
          Unexpected output from `actor_network`.  Expected `Distribution` objects, but saw output spec: TensorSpec(shape=(4,),                 
          dtype=tf.int32, name=None)
          In call to configurable 'PPOPolicy' (<class 'tf_agents.agents.ppo.ppo_policy.PPOPolicy'>)
          In call to configurable 'PPOAgent' (<class 'tf_agents.agents.ppo.ppo_agent.PPOAgent'>)
          ValueError: Unexpected output from `actor_network`.  Expected `Distribution` objects, but saw output spec: 
          TensorSpec(shape=(4,), dtype=tf.int32, name=None)