input: Write a python function to implement the TextRank algorithm for sentence ranking and return the ranked sentences.
  assert textrank_sentences("This is an example text") == ["This is an example text"]
  assert textrank_sentences("Hello world. This is a test.") == ["Hello world.", "This is a test."]
  assert textrank_sentences("The quick brown fox jumps over the lazy dog. A fast blue hare leaps over a sleepy cat.") == ["The quick brown fox jumps over the lazy dog.", "A fast blue hare leaps over a sleepy cat."]
output: def textrank_sentences(text):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.metrics.pairwise import cosine_similarity
      import numpy as np
      import networkx as nx
      
      # Step 1: Split text into sentences
      sentences = text.split('. ')
      if sentences[-1] == '':
          sentences = sentences[:-1]
  
      # Step 2: Create a matrix of token counts
      vectorizer = CountVectorizer().fit_transform(sentences)
      vectors = vectorizer.toarray()
  
      # Step 3: Compute cosine similarity matrix
      cosine_matrix = cosine_similarity(vectors)
  
      # Step 4: Create a graph and apply PageRank
      graph = nx.from_numpy_array(cosine_matrix)
      scores = nx.pagerank(graph)
  
      # Step 5: Rank sentences based on scores
      ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)
      ranked_sentences = [s for _, s in ranked_sentences]
  
      return ranked_sentences