input: Write a python function to find the most relevant document to a query based on TF-IDF scores.
  assert find_most_relevant_document(['New running shoes perfect for running', 'The best fitting shoes for running', 'Running is a great way to exercise'], 'running shoes') == 'New running shoes perfect for running'
  assert find_most_relevant_document(['Cats are cute', 'Dogs are great', 'Birds are amazing'], 'cute') == 'Cats are cute'
  assert find_most_relevant_document(['Python programming', 'Java programming', 'C++ programming'], 'Python') == 'Python programming'
output: from sklearn.feature_extraction.text import TfidfVectorizer
  import numpy as np
  
  def find_most_relevant_document(documents, query):
      vectorizer = TfidfVectorizer()
      tfidf_matrix = vectorizer.fit_transform(documents + [query])
      
      query_vector = tfidf_matrix[-1]
      document_vectors = tfidf_matrix[:-1]
  
      cosine_similarities = document_vectors @ query_vector.T
      most_relevant_index = np.argmax(cosine_similarities.toarray())
      
      return documents[most_relevant_index]