tf.data.Options(), dataset options to use. Note that when
shuffle_files is True and no seed is defined, deterministic will be set
to False internally, unless it is defined here.
If True (default) and the dataset satisfy the right
conditions (dataset small enough, files not shuffled,...) the dataset will
be cached during the first iteration (through ds = ds.cache()).
If True, examples dict in tf.data.Dataset will have an
additional key 'tfds_id': tf.Tensor(shape=(), dtype=tf.string)
containing the example unique identifier (e.g.
Note: IDs might changes in future version of TFDS.
tf.distribute.InputContext, if set, each worker will read a
different set of file. For more info, see the
Note: * Each workers will always read the same subset of files.
shuffle_files only shuffle files within each worker. * If
info.splits[split].num_shards < input_context.num_input_pipelines, an
error will be raised, as some workers would be empty.
Function with signature List[FileDict] ->
List[FileDict], which takes the list of
dict(file: str, take: int, skip: int) and returns the modified version
to read. This can be used to sort/shuffle the shards to read in a custom
order, instead of relying on shuffle_files=True.
If False (default), add a ds.prefetch() op at the end.
Might be set for performance optimization in some cases (e.g. if you're
already calling ds.prefetch() at the end of your pipeline)
The number of parallel calls for decoding
record. By default using tf.data's AUTOTUNE.
The number of parallel calls for
interleaving files. By default using tf.data's AUTOTUNE.
When True (default), an exception is raised if
shuffling or interleaving are used on an ordered dataset.
When True (default), an exception is raised if at the
end of an Epoch the number of read examples does not match the expected
number from dataset metadata. A power user would typically want to set
False if input files have been tempered with and they don't mind missing
records or have too many of them.
Instance of tensorflow_datasets.core.utils.read_config._MISSING