import tensorflow as tf
import numpy as np
rule_tag = tf.constant([[7],[5],[5],[5],[7]])
preds = tf.constant([[0.9],[1.5],[1.1]])
print(rule_tag)
eq = tf.equal(tf.constant(7, dtype=tf.int32), rule_tag)
indices = tf.reduce_max(tf.where(eq, tf.ones_like(rule_tag), tf.zeros_like(rule_tag)), axis=1)
 
indices2 = tf.stack([tf.range(tf.shape(indices)[0]), tf.cast(indices, tf.int32)], axis=1) 
preds_add = tf.concat([preds, tf.zeros([tf.shape(preds)[0],1])], axis=1)
sess = tf.Session()
print(sess.run(preds_add))
 
# Learns best fit is W: [0.1], b: [0.3]# your code goes here

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