class_docstr: DSSM Model preprocessor.
docstr: Fit pre-processing context for transformation.
  
  :param verbose: Verbosity.
  :param data_pack: data_pack to be preprocessed.
  :return: class:`DSSMPreprocessor` instance.
docstr_tok: ["Fit","pre-processing","context","for","transformation",".",":","param","verbose",":","Verbosity",".",":","param","data_pack",":","data_pack","to","be","preprocessed",".",":","return",":","class",":","`","DSSMPreprocessor","`","instance","."]
code: def fit(self, data_pack: DataPack, verbose: int = 1):
      """
      Fit pre-processing context for transformation.
  
      :param verbose: Verbosity.
      :param data_pack: data_pack to be preprocessed.
      :return: class:`DSSMPreprocessor` instance.
      """
  
      func = chain_transform(self._default_units())
      data_pack = data_pack.apply_on_text(func, verbose=verbose)
      vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
  
      self._context["vocab_unit"] = vocab_unit
      vocab_size = len(vocab_unit.state["term_index"])
      self._context["vocab_size"] = vocab_size
      self._context["embedding_input_dim"] = vocab_size
      self._context["input_shapes"] = [(vocab_size,), (vocab_size,)]
      return self
  
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 2 for calling the DSSMPreprocessor obj's underlying member method code with example usage: obj.fit(data_pack, verbose) and returns: self