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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def sentence_similarity(sentence1, sentence2):
 """ compute the sentence similarity using Wordnet """

    # Tokenize and tag
    sentence1 = pos_tag(word_tokenize(sentence1))
    sentence2 = pos_tag(word_tokenize(sentence2))

    # Get the synsets for the tagged words
    synsets1 = [tagged_to_synset(*tagged_word) for tagged_word in sentence1]
    synsets2 = [tagged_to_synset(*tagged_word) for tagged_word in sentence2]

    # Filter out the Nones
    synsets1 = [ss for ss in synsets1 if ss]
    synsets2 = [ss for ss in synsets2 if ss]
    #print(synsets1)
    #print(synsets2)
    score, count = 0.0, 0.0

    # For each word in the first sentence
    for synset in synsets1:
        # Get the similarity value of the most similar word in the other sentence

        best_score=[synset.path_similarity(ss) for ss in synsets2 if synset.path_similarity(ss)]

        # Check that the similarity could have been computed
        if best_score:
            score += max(best_score)
            count += 1

    # Average the values
    if count > 0:
        score /= count
    else:
        score = 0

    return score