class_docstr: DAta Record to Text Generation.
class_docstr_tok: ["DAta","Record","to","Text","Generation","."]
code: def _info(self):
      return tfds.core.DatasetInfo(
          builder=self,
          # This is the description that will appear on the datasets page.
          description=_DESCRIPTION,
          # tfds.features.FeatureConnectors
          features=tfds.features.FeaturesDict(
              {
                  "input_text": {
                      "table": tfds.features.Sequence(
                          {  # Each row will be one triple fact.
                              # we'll only have subject/predicate/object headers
                              "column_header": np.str_,
                              "row_number": np.int16,
                              "content": np.str_,
                          }
                      ),
                  },
                  "target_text": np.str_,
              }
          ),
          supervised_keys=("input_text", "target_text"),
          # Homepage of the dataset for documentation
          homepage="https://github.com/Yale-LILY/dart",
          citation=_CITATION,
      )
  
file_path: datasets/tensorflow_datasets/structured/dart/dart.py
imports: from __future__ import annotations
  import json
  import os
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling the Dart obj's underlying member method code with example usage: obj._info() and returns: tfds