code: def transform(self, texts, y=None):
      tokenizer = RegexpTokenizer(r"[a-z]+|\d+")
  
      tokenized_texts = []
      stoplist = []
  
      if self.ignore_stopwords:
          stoplist = stopwords.words("english")
  
      for text in texts:
          tokenized_text = []
          for word in tokenizer.tokenize(text.lower()):
              if word not in stoplist:
                  tokenized_text.append(word.strip())
  
          tokenized_texts.append(tokenized_text)
      return tokenized_texts
  
file_path: neupy/examples/mlp/imdb_review_classification/src/preprocessing.py
imports: from nltk.corpus import stopwords
  from nltk.tokenize import RegexpTokenizer
  from sklearn.base import TransformerMixin
  from sklearn.preprocessing import FunctionTransformer
label_desc: Use image node_id 2 for calling the TokenizeText obj's underlying member method code with example usage: obj.transform(texts, y) and returns: tokenized_texts