input: Write a python function to tokenize text from a file and tag parts of speech using nltk library.
  assert tokenize_and_tag('text.txt') == [('The', 'DT'), ('quick', 'JJ'), ('brown', 'NN'), ('fox', 'NN'), ('jumps', 'VBZ'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'JJ'), ('dog', 'NN'), ('.', '.')]
  assert tokenize_and_tag('another_text.txt') == [('She', 'PRP'), ('sells', 'VBZ'), ('seashells', 'NNS'), ('by', 'IN'), ('the', 'DT'), ('seashore', 'NN'), ('.', '.')]
output: def tokenize_and_tag(file_path):
      import nltk
      from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      from nltk.tag import pos_tag
      
      nltk.download('punkt')
      nltk.download('averaged_perceptron_tagger')
      nltk.download('stopwords')
  
      with open(file_path, 'r') as file:
          text = file.read()
  
      tokens = word_tokenize(text)
      tagged_tokens = pos_tag(tokens)
      
      return tagged_tokens