import tensorflow as tf

        print("----feature_mat-----")
        print(feature_mat)
        # exit()

        # Temporarily crush the feature_mat's dimensions
        feature_mat = tf.reshape(feature_mat, [-1, config.n_inputs])  # 9
        # New feature_mat's shape: [time_steps*batch_size, n_inputs]  # 128 * batch_size, 9

        # Linear activation, reshaping inputs to the LSTM's number of hidden:
        hidden = tf.nn.relu(tf.matmul(
            feature_mat, config.W['hidden']
        ) + config.biases['hidden'])
        # New feature_mat (hidden) shape: [time_steps*batch_size, n_hidden] [128*batch_size, 32]

        print("--n_steps--")
        print(config.n_steps)
        print("--hidden--")
        print(hidden)
