Why my UNQ_C7 incorrect while I got same expected output?

I got the same output in UNQ_C7 as expected output but grader out put says mine incorrect.


GRADED FUNCTION: viterbi_backward

def viterbi_backward(best_probs, best_paths, corpus, states):
This function returns the best path.

# Get the number of words in the corpus
# which is also the number of columns in best_probs, best_paths
m = best_paths.shape[1] 

# Initialize array z, same length as the corpus
z = [None] * m

# Get the number of unique POS tags
num_tags = best_probs.shape[0]

# Initialize the best probability for the last word
best_prob_for_last_word = float('-inf')

# Initialize pred array, same length as corpus
pred = [None] * m

### START CODE HERE (Replace instances of 'None' with your code) ###
## Step 1 ##

# Go through each POS tag for the last word (last column of best_probs)
# in order to find the row (POS tag integer ID) 
# with highest probability for the last word
for k in range(num_tags): # complete this line

    # If the probability of POS tag at row k 
    # is better than the previously best probability for the last word:
    if best_probs[k, m-1] >= best_prob_for_last_word: # complete this line
        # Store the new best probability for the last word
        best_prob_for_last_word = best_probs[k, m-1]

        # Store the unique integer ID of the POS tag
        # which is also the row number in best_probs
        z[m - 1] = k
# Convert the last word's predicted POS tag
# from its unique integer ID into the string representation
# using the 'states' list
# store this in the 'pred' array for the last word
pred[m - 1] = states[z[m - 1]]

## Step 2 ##
# Find the best POS tags by walking backward through the best_paths
# From the last word in the corpus to the 0th word in the corpus
for i in range(m-1, 0, -1): # complete this line
    # Retrieve the unique integer ID of
    # the POS tag for the word at position 'i' in the corpus
    pos_tag_for_word_i = best_paths[z[i], i]
    # In best_paths, go to the row representing the POS tag of word i
    # and the column representing the word's position in the corpus
    # to retrieve the predicted POS for the word at position i-1 in the corpus
    z[i - 1] = best_paths[pos_tag_for_word_i, i]
    # Get the previous word's POS tag in string form
    # Use the 'states' list, 
    # where the key is the unique integer ID of the POS tag,
    # and the value is the string representation of that POS tag
    pred[i - 1] = states[z[i-1]]
 ### END CODE HERE ###

here it says better than, have you tried to change it to ‘>’?

You might need to restart and run all the cells again before submission.

I did “restart and rerun” every time. It doesn’t help at all.

For future reference, this line of code is incorrect. I got the same problem when the variable was defined like this. It took a while to see that I should not take the index-value of the previous column as is being done with this code.

I have a similar problem; did somebody figure out why the code is not working? any suggestion?