def METHOD_NAME(
    train_client_spec: client_spec.ClientSpec,
    eval_client_spec: Optional[client_spec.ClientSpec] = None,
    only_digits: bool = False,
    cache_dir: Optional[str] = None,
    use_synthetic_data: bool = False,
) -> baseline_task.BaselineTask:
  """Creates a baseline task for autoencoding on EMNIST.
  This task involves performing autoencoding on the EMNIST dataset using a
  densely connected bottleneck network. The model uses 8 layers of widths
  `[1000, 500, 250, 30, 250, 500, 1000, 784]`, with the final layer being the
  output layer. Each layer uses a sigmoid activation function, except the
  smallest layer, which uses a linear activation function.
  The goal of the task is to minimize the mean squared error between the input
  to the network and the output of the network.
  Args:
    train_client_spec: A `tff.simulation.baselines.ClientSpec` specifying how to
      preprocess train client data.
    eval_client_spec: An optional `tff.simulation.baselines.ClientSpec`
      specifying how to preprocess evaluation client data. If set to `None`, the
      evaluation datasets will use a batch size of 64 with no extra
      preprocessing.
    only_digits: A boolean indicating whether to use the full EMNIST-62 dataset
      containing 62 alphanumeric classes (`True`) or the smaller EMNIST-10
      dataset with only 10 numeric classes (`False`).
    cache_dir: An optional directory to cache the downloadeded datasets. If
      `None`, they will be cached to `~/.tff/`.
    use_synthetic_data: A boolean indicating whether to use synthetic EMNIST
      data. This option should only be used for testing purposes, in order to
      avoid downloading the entire EMNIST dataset.
  Returns:
    A `tff.simulation.baselines.BaselineTask`.
  """
  if use_synthetic_data:
    synthetic_data = emnist.get_synthetic()
    emnist_train = synthetic_data
    emnist_test = synthetic_data
  else:
    emnist_train, emnist_test = emnist.load_data(
        only_digits=only_digits, cache_dir=cache_dir
    )
  return create_autoencoder_task_from_datasets(
      train_client_spec, eval_client_spec, emnist_train, emnist_test
  )