import tensorflow as tf

        b = tf.get_variable('b', [input_size_y], initializer=tf.constant_initializer(0.0))

    rnn_outputs = tf.reshape(rnn_outputs, [-1, state_size])
    predictions = tf.matmul(rnn_outputs, W) + b
    yy = tf.reshape(y, [-1, input_size_y])   #batch_size*num_steps when yo udefine a placeholder in Tensorflow, the shape of the input during the session should be the same as the shape of the plcae holder
    "Mean squared error loss"
    loss=tf.reduce_mean(tf.square(tf.reshape(predictions,[-1])-tf.reshape(yy,[-1])))

