class_docstr: Glove Preprocessor to split tokens that can be passed into glove embeddings 
docstr: Convert a list of text into a dataframe containing padded token ids,
  masks distinguishing word tokens from pads, and word token counts for
  each text in the list.
  :param data: list of strings (e.g. sentences)
  :type data: list
  :return: tokens (pd.Dataframe): a dataframe containing
      lists of word token ids, pad/word masks, and token counts
      for each string in the list
  :rtype: pandas dataframe
code: def preprocess(self, data) -> pd.DataFrame:
      """Convert a list of text into a dataframe containing padded token ids,
      masks distinguishing word tokens from pads, and word token counts for
      each text in the list.
      :param data: list of strings (e.g. sentences)
      :type data: list
      :return: tokens (pd.Dataframe): a dataframe containing
          lists of word token ids, pad/word masks, and token counts
          for each string in the list
      :rtype: pandas dataframe
      """
      token_lists = []
      masks = []
      counts = []
      for sentence in data:
          token_list, mask = self.generate_tokens(sentence)
          token_lists.append(token_list)
          masks.append(mask)
          counts.append(np.sum(mask))
      tokens = pd.DataFrame(
          {"tokens": token_lists, "mask": masks, "counts": counts}
      )
      return tokens
  
file_path: interpret-text/python/interpret_text/experimental/common/preprocessor/glove_preprocessor.py
imports: import numpy
  import pandas
  from typing import Any, Iterable, List
  from interpret_text.experimental.common.base_explainer import BaseTextPreprocessor
label_desc: Use image node_id 3 for calling the GlovePreprocessor obj's underlying member method code with example usage: obj.preprocess(data) and returns: tokens