code: def _standard_template(info: cli_utils.DatasetInfo) -> str:
      """Returns a template for a `tfds.core.GeneratorBasedBuilder`."""
      content = textwrap.dedent(
          f'''\
        """{info.name} dataset."""
  
        import {info.tfds_api} as tfds
  
  
        class Builder(tfds.core.GeneratorBasedBuilder):
          """DatasetBuilder for {info.name} dataset."""
  
          VERSION = tfds.core.Version('1.0.0')
          RELEASE_NOTES = {{
              '1.0.0': 'Initial release.',
          }}
  
          def _info(self) -> tfds.core.DatasetInfo:
            """Returns the dataset metadata."""
            # {info.todo}: Specifies the tfds.core.DatasetInfo object
            return self.dataset_info_from_configs(
                features=tfds.features.FeaturesDict({{
                    # These are the features of your dataset like images, labels ...
                    'image': tfds.features.Image(shape=(None, None, 3)),
                    'label': tfds.features.ClassLabel(names=['no', 'yes']),
                }}),
                # If there's a common (input, target) tuple from the
                # features, specify them here. They'll be used if
                # `as_supervised=True` in `builder.as_dataset`.
                supervised_keys=('image', 'label'),  # Set to `None` to disable
                homepage='https://dataset-homepage/',
            )
  
          def _split_generators(self, dl_manager: tfds.download.DownloadManager):
            """Returns SplitGenerators."""
            # {info.todo}: Downloads the data and defines the splits
            path = dl_manager.download_and_extract('https://todo-data-url')
  
            # {info.todo}: Returns the Dict[split names, Iterator[Key, Example]]
            return {{
                'train': self._generate_examples(path / 'train_imgs'),
            }}
  
          def _generate_examples(self, path):
            """Yields examples."""
            # {info.todo}: Yields (key, example) tuples from the dataset
            for f in path.glob('*.jpeg'):
              yield 'key', {{
                  'image': f,
                  'label': 'yes',
              }}
        '''
      )
      return content
  
imports: import textwrap
  from tensorflow_datasets.scripts.cli import cli_utils
label_desc: Use image node_id 2 for calling a global function with example usage: _standard_template(info) and returns: content