class_docstr: Lambada as reading comprehension task.
class_docstr_tok: ["Lambada","as","reading","comprehension","task","."]
docstr: Return a dict for encoding and decoding inference input/output.
  
  Args:
    data_dir: data directory
  
  Returns:
    A dict of <feature name, TextEncoder>.
code: def feature_encoders(self, data_dir):
      """Return a dict for encoding and decoding inference input/output.
  
      Args:
        data_dir: data directory
  
      Returns:
        A dict of <feature name, TextEncoder>.
  
      """
      txt_encoder = self.get_or_create_vocab(
          data_dir, None, force_get=True
      )
      label_encoder = self.get_labels_encoder(data_dir)
      return {"inputs": txt_encoder, "targets": label_encoder}
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import csv
  import os
  import tarfile
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 10 for calling the LambadaRc obj's underlying member method code with example usage: obj.feature_encoders(data_dir) and returns: dict