def _create_network(self, input_view, input_feature, use_conv=True, 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]

        if use_conv:  # convolution
            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)
        else:         # fully connected
            flatten_view = tf.reshape(input_view, [-1, np.prod([v.value for v in input_view.shape[1:]])])
            h_view = tf.layers.dense(flatten_view, units=hidden_size[0], activation=tf.nn.relu)

        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)

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

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

        return qvalues
define computation graph of network

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