class SpacyWordTokenizer():
    """
    A Tokenizer splits strings into word tokens.
    """
    def __init__(self):
        # Import is here it's slow, and can be unnecessary.
        import spacy
        self.en_nlp = spacy.load('en')

    # def tokenize(self, sentence: str) -> List[str]:
    def tokenize(self, sentence):
        return [str(token.lower_) for token in self.en_nlp.tokenizer(sentence)]

    # def get_words_for_indexer(self, text: str) -> List[str]:
    def get_words_for_indexer(self, text):
        return self.tokenize(text)

    # def index_text(self, text: str, data_indexer: DataIndexer) -> List:
    def index_text(self, text, data_indexer):
        return [data_indexer.get_word_index(word) for word in
                self.get_words_for_indexer(text)]

class SpacyWordTokenizer():
    """
    A Tokenizer splits strings into word tokens.
    """
    def __init__(self):
        # Import is here it's slow, and can be unnecessary.
        import spacy
        self.en_nlp = spacy.load('en')

    def tokenize(self, sentence):
        return [str(token.lower_) for token in self.en_nlp.tokenizer(sentence)]

    def get_words_for_indexer(self, text):
        return self.tokenize(text)

    def index_text(self, text, data_indexer):
        return [data_indexer.get_word_index(word) for word in
                self.get_words_for_indexer(text)]

Remove unnecesssary comments of old function signatures
Remove unnecesssary comments of old function signatures

  class SpacyWordTokenizer():
      """
      A Tokenizer splits strings into word tokens.
      """
      def __init__(self):
          # Import is here it's slow, and can be unnecessary.
          import spacy
          self.en_nlp = spacy.load('en')
  
-     # def tokenize(self, sentence: str) -> List[str]:
      def tokenize(self, sentence):
          return [str(token.lower_) for token in self.en_nlp.tokenizer(sentence)]
  
-     # def get_words_for_indexer(self, text: str) -> List[str]:
      def get_words_for_indexer(self, text):
          return self.tokenize(text)
  
-     # def index_text(self, text: str, data_indexer: DataIndexer) -> List:
      def index_text(self, text, data_indexer):
          return [data_indexer.get_word_index(word) for word in
                  self.get_words_for_indexer(text)]
  
Remove unnecesssary comments of old function signatures
## Code Before:
class SpacyWordTokenizer():
    """
    A Tokenizer splits strings into word tokens.
    """
    def __init__(self):
        # Import is here it's slow, and can be unnecessary.
        import spacy
        self.en_nlp = spacy.load('en')

    # def tokenize(self, sentence: str) -> List[str]:
    def tokenize(self, sentence):
        return [str(token.lower_) for token in self.en_nlp.tokenizer(sentence)]

    # def get_words_for_indexer(self, text: str) -> List[str]:
    def get_words_for_indexer(self, text):
        return self.tokenize(text)

    # def index_text(self, text: str, data_indexer: DataIndexer) -> List:
    def index_text(self, text, data_indexer):
        return [data_indexer.get_word_index(word) for word in
                self.get_words_for_indexer(text)]

## Instruction:
Remove unnecesssary comments of old function signatures
## Code After:
class SpacyWordTokenizer():
    """
    A Tokenizer splits strings into word tokens.
    """
    def __init__(self):
        # Import is here it's slow, and can be unnecessary.
        import spacy
        self.en_nlp = spacy.load('en')

    def tokenize(self, sentence):
        return [str(token.lower_) for token in self.en_nlp.tokenizer(sentence)]

    def get_words_for_indexer(self, text):
        return self.tokenize(text)

    def index_text(self, text, data_indexer):
        return [data_indexer.get_word_index(word) for word in
                self.get_words_for_indexer(text)]
