(
      self,
      md_version,
      meta_split,
      relative_label,
      shuffle_buffer_size = 0,
      shuffle_seed = None,
      num_prefetch = 0,
      **as_dataset_kwargs)
return class_dataset
Creates a class dataset.

    Args:
      md_version: Meta-Dataset version in {'v1', 'v2'}.
      meta_split: meta-split (case-insensitive) in {'train', 'valid', 'test'}.
      relative_label: label, relative to the meta-split.
      shuffle_buffer_size: shuffle buffer size for the class dataset.
      shuffle_seed: shuffle buffer random seed.
      num_prefetch: number of examples to prefetch.
      **as_dataset_kwargs: kwargs passed to the `as_dataset` method.

    Returns:
      The class dataset.

    Raises:
      ValueError, if the source does not provide `meta_split` in `md_version`.
Creates a class dataset.
Creates a class dataset .
def as_class_dataset(
      self,
      md_version,
      meta_split,
      relative_label,
      shuffle_buffer_size = 0,
      shuffle_seed = None,
      num_prefetch = 0,
      **as_dataset_kwargs):
    """Creates a class dataset.

    Args:
      md_version: Meta-Dataset version in {'v1', 'v2'}.
      meta_split: meta-split (case-insensitive) in {'train', 'valid', 'test'}.
      relative_label: label, relative to the meta-split.
      shuffle_buffer_size: shuffle buffer size for the class dataset.
      shuffle_seed: shuffle buffer random seed.
      num_prefetch: number of examples to prefetch.
      **as_dataset_kwargs: kwargs passed to the `as_dataset` method.

    Returns:
      The class dataset.

    Raises:
      ValueError, if the source does not provide `meta_split` in `md_version`.
    """
    start, stop = self.get_start_stop(md_version, meta_split, relative_label)
    class_dataset = self.as_dataset(
        split=f'all_classes[{start}:{stop}]', **as_dataset_kwargs)
    if num_prefetch > 0:
      class_dataset = class_dataset.prefetch(num_prefetch)
    shuffle_buffer_size = min(stop - start, shuffle_buffer_size)
    if shuffle_buffer_size > 1:
      class_dataset = class_dataset.shuffle(
          buffer_size=shuffle_buffer_size,
          seed=shuffle_seed,
          reshuffle_each_iteration=True)
    return class_dataset
def as_class_dataset ( self , md_version , meta_split , relative_label , shuffle_buffer_size = 0 , shuffle_seed = None , num_prefetch = 0 , * * as_dataset_kwargs ) : start , stop = self . get_start_stop ( md_version , meta_split , relative_label ) class_dataset = self . as_dataset ( split = f'all_classes[{start}:{stop}]' , * * as_dataset_kwargs ) if num_prefetch > 0 : class_dataset = class_dataset . prefetch ( num_prefetch ) shuffle_buffer_size = min ( stop - start , shuffle_buffer_size ) if shuffle_buffer_size > 1 : class_dataset = class_dataset . shuffle ( buffer_size = shuffle_buffer_size , seed = shuffle_seed , reshuffle_each_iteration = True ) return class_dataset