class_docstr: CDSSM Model preprocessor.
docstr: Fit pre-processing context for transformation.
  
  :param verbose: Verbosity.
  :param data_pack: Data_pack to be preprocessed.
  :return: class:`CDSSMPreprocessor` instance.
docstr_tok: ["Fit","pre-processing","context","for","transformation",".",":","param","verbose",":","Verbosity",".",":","param","data_pack",":","Data_pack","to","be","preprocessed",".",":","return",":","class",":","`","CDSSMPreprocessor","`","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:`CDSSMPreprocessor` instance.
      """
      fit_units = self._default_units() + [units.NgramLetter()]
      func = chain_transform(fit_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["input_shapes"] = [
          (self._fixed_length_left, vocab_size),
          (self._fixed_length_right, vocab_size),
      ]
      return self
  
file_path: MatchZoo/matchzoo/preprocessors/cdssm_preprocessor.py
imports: from tqdm import tqdm
  from .None import units
  from .chain_transform import chain_transform
  from matchzoo import DataPack
  from matchzoo.engine.base_preprocessor import BasePreprocessor
  from .build_vocab_unit import build_vocab_unit
label_desc: Use image node_id 2 for calling the CDSSMPreprocessor obj's underlying member method code with example usage: obj.fit(data_pack, verbose) and returns: self