input: Write a python function to remove stopwords and identify topics from a list of tokenized blog posts.
  assert remove_stopwords_and_identify_topics([['This', 'is', 'a', 'test'], ['Another', 'test', 'post']], {'this', 'is', 'a'}) == (['test', 'Another', 'test', 'post'], {'test': 2, 'Another': 1, 'post': 1})
  assert remove_stopwords_and_identify_topics([['Hello', 'world'], ['Hello', 'again'], ['Another', 'test']], {'hello', 'world'}) == (['again', 'Another', 'test'], {'again': 1, 'Another': 1, 'test': 1})
  assert remove_stopwords_and_identify_topics([['Python', 'is', 'great'], ['I', 'love', 'coding']], {'is', 'i'}) == (['Python', 'great', 'love', 'coding'], {'Python': 1, 'great': 1, 'love': 1, 'coding': 1})
output: def remove_stopwords_and_identify_topics(posts, stopwords):
      filtered_words = []
      word_count = {}
      
      for post in posts:
          for word in post:
              word_lower = word.lower()
              if word_lower not in stopwords:
                  filtered_words.append(word)
                  if word not in word_count:
                      word_count[word] = 1
                  else:
                      word_count[word] += 1
      
      return filtered_words, word_count