def _create_network(self, input_view, input_feature, reuse=None):
        """define computation graph of network

        Parameters
        ----------
        input_view: tf.tensor
        input_feature: tf.tensor
            the input tensor
        """
        kernel_num  = [32, 32]
        hidden_size = [256]

        # conv
        h_conv1 = tf.layers.conv2d(input_view, filters=kernel_num[0], kernel_size=3,
                                   activation=tf.nn.relu, name="conv1", reuse=reuse)
        h_conv2 = tf.layers.conv2d(h_conv1, filters=kernel_num[1], kernel_size=3,
                                   activation=tf.nn.relu, name="conv2", reuse=reuse)
        flatten_view = tf.reshape(h_conv2, [-1, np.prod([v.value for v in h_conv2.shape[1:]])])
        h_view = tf.layers.dense(flatten_view, units=hidden_size[0], activation=tf.nn.relu,
                                 name="dense_view", reuse=reuse)

        h_emb = tf.layers.dense(input_feature,  units=hidden_size[0], activation=tf.nn.relu,
                                name="dense_emb", reuse=reuse)

        dense = tf.concat([h_view, h_emb], axis=1)

        # RNN
        state_size = hidden_size[0] * 2
        rnn_cell = tf.contrib.rnn.GRUCell(num_units=state_size)

        rnn_in = tf.reshape(dense, shape=[self.batch_size_ph, self.unroll_step_ph, state_size])
        state_in = rnn_cell.zero_state(self.batch_size_ph, tf.float32)
        rnn, rnn_state = tf.nn.dynamic_rnn(
            cell=rnn_cell, inputs=rnn_in, dtype=tf.float32, initial_state=state_in
        )
        rnn = tf.reshape(rnn, shape=[-1, state_size])

        if self.use_dueling:
            value = tf.layers.dense(dense, units=1, name="dense_value", reuse=reuse)
            advantage = tf.layers.dense(dense, units=self.num_actions, use_bias=False,
                                        name="dense_advantage", reuse=reuse)

            qvalues = value + advantage - tf.reduce_mean(advantage, axis=1, keep_dims=True)
        else:
            qvalues = tf.layers.dense(rnn, units=self.num_actions)

        self.state_size = state_size
        return qvalues, state_in, rnn_state
define computation graph of network

        Parameters
        ----------
        input_view: tf.tensor
        input_feature: tf.tensor
            the input tensor
        
python/magent/builtin/tf_model/drqn.py