input: Write a python function to tokenize a sentence, perform POS tagging, and count the number of letters in the sentence.
  assert process_sentence('This is an example sentence.') == ([(('This', 'DT'), ('is', 'VBZ'), ('an', 'DT'), ('example', 'NN'), ('sentence', 'NN'))], 21)
  assert process_sentence('Python programming is fun.') == ([(('Python', 'NNP'), ('programming', 'NN'), ('is', 'VBZ'), ('fun', 'NN'))], 24)
  assert process_sentence('I love coding.') == ([(('I', 'PRP'), ('love', 'VBP'), ('coding', 'VBG'))], 11)
output: import nltk
  from nltk.tokenize import word_tokenize
  from nltk import pos_tag
  
  nltk.download('punkt')
  nltk.download('averaged_perceptron_tagger')
  
  def process_sentence(sentence):
      # Tokenize the sentence
      tokens = word_tokenize(sentence)
      
      # Perform POS tagging
      pos_tags = pos_tag(tokens)
      
      # Count the number of letters in the sentence
      letter_count = sum(c.isalpha() for c in sentence)
      
      return (pos_tags, letter_count)