tfds_load: Loads the named dataset into a TensorFlow Dataset
In rstudio/tfds: Interface to 'TensorFlow' Collection of Datasets

Loads the named dataset into a TensorFlow Dataset

tfds_load(
  name,
  split = NULL,
  data_dir = NULL,
  batch_size = NULL,
  download = TRUE,
  shuffle_files = FALSE,
  as_supervised = FALSE,
  decoders = NULL,
  read_config = NULL,
  builder_kwargs = NULL,
  download_and_prepare_kwargs = NULL,
  as_dataset_kwargs = NULL,
  try_gcs = FALSE
)

`name`	`str`, the registered name of the `DatasetBuilder` (the snake case version of the class name). This can be either `"dataset_name"` or `"dataset_name/config_name"` for datasets with `BuilderConfig`s. As a convenience, this string may contain comma-separated keyword arguments for the builder. For example `"foo_bar/a=True,b=3"` would use the `FooBar` dataset passing the keyword arguments `a=True` and `b=3` (for builders with configs, it would be `"foo_bar/zoo/a=True,b=3"` to use the `"zoo"` config and pass to the builder keyword arguments `a=True` and `b=3`).
`split`	`tfds.Split` or `str`, which split of the data to load. If None, will return a `dict` with all splits (typically `tfds.Split.TRAIN` and `tfds.Split.TEST`).
`data_dir`	`str` (optional), directory to read/write data. Defaults to "~/tensorflow_datasets".
`batch_size`	`int`, if set, add a batch dimension to examples. Note that variable length features will be 0-padded. If `batch_size=-1`, will return the full dataset as `tf.Tensor`s.
`download`	`bool` (optional), whether to call `tfds.core.DatasetBuilder.download_and_prepare` before calling `tf.DatasetBuilder.as_dataset`. If `False`, data is expected to be in `data_dir`. If `True` and the data is already in `data_dir`, `download_and_prepare` is a no-op.
`shuffle_files`	`bool`, whether to shuffle the input files. Defaults to `False`.
`as_supervised`	`bool`, if `True`, the returned `tf.data.Dataset` will have a 2-tuple structure `(input, label)` according to `builder.info.supervised_keys`. If `False`, the default, the returned `tf.data.Dataset` will have a dictionary with all the features.
`decoders`	Nested dict of `Decoder` objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info.
`read_config`	`tfds.ReadConfig`, additional options to configure the input pipeline (e.g. `seed`, `num parallel reads`,...).
`builder_kwargs`	`dict` (optional), keyword arguments to be passed to the `tfds.core.DatasetBuilder` constructor. `data_dir` will be passed through by default.
`download_and_prepare_kwargs`	`dict` (optional) keyword arguments passed to `tfds.core.DatasetBuilder.download_and_prepare` if `download=True`. Allow to control where to download and extract the cached data. If not set, cache_dir and manual_dir will automatically be deduced from data_dir.
`as_dataset_kwargs`	`dict` (optional), keyword arguments passed to `tfds.core.DatasetBuilder.as_dataset`.
`try_gcs`	`bool`, if True, tfds.load will see if the dataset exists on the public GCS bucket before building it locally.