class_docstr: Problem spec for WMT En-Cs character-based translation.
class_docstr_tok: ["Problem","spec","for","WMT","En-Cs","character-based","translation","."]
code: def generate_samples(self, data_dir, tmp_dir, dataset_split):
      train = dataset_split == problem.DatasetSplit.TRAIN
      datasets = _ENCS_TRAIN_DATASETS if train else _ENCS_TEST_DATASETS
      tag = "train" if train else "dev"
      data_path = translate.compile_data(
          tmp_dir, datasets, "wmt_encs_chr_%s" % tag
      )
      return text_problems.text2text_txt_iterator(
          data_path + ".lang1", data_path + ".lang2"
      )
  
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 2 for calling the TranslateEncsWmtCharacters obj's underlying member method code with example usage: obj.generate_samples(data_dir, tmp_dir, dataset_split) and returns: text_problems