class_docstr: Problem spec for WMT English-Czech translation.
class_docstr_tok: ["Problem","spec","for","WMT","English-Czech","translation","."]
code: def vocab_data_files(self):
      datasets = self.source_data_files(problem.DatasetSplit.TRAIN)
      vocab_datasets = []
      if datasets[0][0].endswith("data-plaintext-format.tar"):
          vocab_datasets.append(
              [
                  datasets[0][0],
                  [
                      "%s-compiled-train.lang1" % self.name,
                      "%s-compiled-train.lang2" % self.name,
                  ],
              ]
          )
          datasets = datasets[1:]
      vocab_datasets += [
          [item[0], [item[1][0], item[1][1]]] for item in datasets
      ]
      return vocab_datasets
  
file_path: tensor2tensor/tensor2tensor/data_generators/translate_encs.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.data_generators import translate
  from tensor2tensor.utils import registry
label_desc: Use image node_id 3 for calling the TranslateEncsWmt32k obj's underlying member method code with example usage: obj.vocab_data_files() and returns: vocab_datasets