class_docstr: DSSM Model preprocessor.
docstr: Apply transformation on data, create `tri-letter` representation.
  
  :param data_pack: Inputs to be preprocessed.
  :param verbose: Verbosity.
  
  :return: Transformed data as :class:`DataPack` object.
docstr_tok: ["Apply","transformation","on","data",",","create","`","tri-letter","`","representation",".",":","param","data_pack",":","Inputs","to","be","preprocessed",".",":","param","verbose",":","Verbosity",".",":","return",":","Transformed","data","as",":","class",":","`","DataPack","`","object","."]
code: def transform(
      self, data_pack: DataPack, verbose: int = 1
  ) -> DataPack:
      """
      Apply transformation on data, create `tri-letter` representation.
  
      :param data_pack: Inputs to be preprocessed.
      :param verbose: Verbosity.
  
      :return: Transformed data as :class:`DataPack` object.
      """
      data_pack = data_pack.copy()
      units_ = self._default_units()
      if self._with_word_hashing:
          term_index = self._context["vocab_unit"].state["term_index"]
          units_.append(units.WordHashing(term_index))
      func = chain_transform(units_)
      data_pack.apply_on_text(func, inplace=True, verbose=verbose)
      return data_pack
  
file_path: MatchZoo/matchzoo/preprocessors/dssm_preprocessor.py
imports: from tqdm import tqdm
  from matchzoo.data_pack import DataPack
  from matchzoo.engine.base_preprocessor import BasePreprocessor
  from .chain_transform import chain_transform
  from .build_vocab_unit import build_vocab_unit
  from .None import units
label_desc: Use image node_id 3 for calling the DSSMPreprocessor obj's underlying member method code with example usage: obj.transform(data_pack, verbose) and returns: data_pack