def load(name,
         split=None,
         data_dir=None,
         batch_size=1,
         download=True,
         as_supervised=False,
         with_info=False,
         builder_kwargs=None,
         download_and_prepare_kwargs=None,
         as_dataset_kwargs=None,
         try_gcs=False):
  """Loads the named dataset into a `tf.data.Dataset`.

  If `split=None` (the default), returns all splits for the dataset. Otherwise,
  returns the specified split.

  `load` is a convenience method that fetches the `tfds.core.DatasetBuilder` by
  string name, optionally calls `DatasetBuilder.download_and_prepare`
  (if `download=True`), and then calls `DatasetBuilder.as_dataset`.
  This is roughly equivalent to:

  ```
  builder = tfds.builder(name, data_dir=data_dir, **builder_kwargs)
  if download:
    builder.download_and_prepare(**download_and_prepare_kwargs)
  ds = builder.as_dataset(
      split=split, as_supervised=as_supervised, **as_dataset_kwargs)
  if with_info:
    return ds, builder.info
  return ds
  ```

  If you'd like NumPy arrays instead of `tf.data.Dataset`s or `tf.Tensor`s,
  you can pass the return value to `tfds.as_numpy`.

  Callers must pass arguments as keyword arguments.

  **Warning**: calling this function might potentially trigger the download
  of hundreds of GiB to disk. Refer to the `download` argument.

  Args:
    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`, set to > 1 to get batches of 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.
    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.
    with_info: `bool`, if True, tfds.load will return the tuple
      (tf.data.Dataset, tfds.core.DatasetInfo) containing the info associated
      with the builder.
    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`. `split` will be passed through by
      default. Example: `{'shuffle_files': True}`.
      Note that shuffle_files is False by default unless
      `split == tfds.Split.TRAIN`.
    try_gcs: `bool`, if True, tfds.load will see if the dataset exists on
      the public GCS bucket before building it locally.

  Returns:
    ds: `tf.data.Dataset`, the dataset requested, or if `split` is None, a
      `dict<key: tfds.Split, value: tfds.data.Dataset>`. If `batch_size=-1`,
      these will be full datasets as `tf.Tensor`s.
    ds_info: `tfds.core.DatasetInfo`, if `with_info` is True, then `tfds.load`
      will return a tuple `(ds, ds_info)` containing dataset information
      (version, features, splits, num_examples,...). Note that the `ds_info`
      object documents the entire dataset, regardless of the `split` requested.
      Split-specific information is available in `ds_info.splits`.
  """
  name, name_builder_kwargs = _dataset_name_and_kwargs_from_name_str(name)
  name_builder_kwargs.update(builder_kwargs or {})
  builder_kwargs = name_builder_kwargs

  # Set data_dir
  if try_gcs and gcs_utils.is_dataset_on_gcs(name):
    data_dir = constants.GCS_DATA_DIR
  elif data_dir is None:
    data_dir = constants.DATA_DIR

  dbuilder = builder(name, data_dir=data_dir, **builder_kwargs)
  if download:
    download_and_prepare_kwargs = download_and_prepare_kwargs or {}
    dbuilder.download_and_prepare(**download_and_prepare_kwargs)

  if as_dataset_kwargs is None:
    as_dataset_kwargs = {}
  as_dataset_kwargs = dict(as_dataset_kwargs)
  as_dataset_kwargs["split"] = split
  as_dataset_kwargs["as_supervised"] = as_supervised
  as_dataset_kwargs["batch_size"] = batch_size

  ds = dbuilder.as_dataset(**as_dataset_kwargs)
  if with_info:
    return ds, dbuilder.info
  return ds
def load ( name , split = None , data_dir = None , batch_size = 1 , download = True , as_supervised = False , with_info = False , builder_kwargs = None , download_and_prepare_kwargs = None , as_dataset_kwargs = None , try_gcs = False ) : name , name_builder_kwargs = _dataset_name_and_kwargs_from_name_str ( name ) name_builder_kwargs . update ( builder_kwargs or { } ) builder_kwargs = name_builder_kwargs if try_gcs and gcs_utils . is_dataset_on_gcs ( name ) : data_dir = constants . GCS_DATA_DIR elif data_dir is None : data_dir = constants . DATA_DIR dbuilder = builder ( name , data_dir = data_dir , ** builder_kwargs ) if download : download_and_prepare_kwargs = download_and_prepare_kwargs or { } dbuilder . download_and_prepare ( ** download_and_prepare_kwargs ) if as_dataset_kwargs is None : as_dataset_kwargs = { } as_dataset_kwargs = dict ( as_dataset_kwargs ) as_dataset_kwargs [ "split" ] = split as_dataset_kwargs [ "as_supervised" ] = as_supervised as_dataset_kwargs [ "batch_size" ] = batch_size ds = dbuilder . as_dataset ( ** as_dataset_kwargs ) if with_info : return ds , dbuilder . info return ds
Loads the named dataset into a `tf.data.Dataset`.

  If `split=None` (the default), returns all splits for the dataset. Otherwise,
  returns the specified split.

  `load` is a convenience method that fetches the `tfds.core.DatasetBuilder` by
  string name, optionally calls `DatasetBuilder.download_and_prepare`
  (if `download=True`), and then calls `DatasetBuilder.as_dataset`.
  This is roughly equivalent to:

  ```
  builder = tfds.builder(name, data_dir=data_dir, **builder_kwargs)
  if download:
    builder.download_and_prepare(**download_and_prepare_kwargs)
  ds = builder.as_dataset(
      split=split, as_supervised=as_supervised, **as_dataset_kwargs)
  if with_info:
    return ds, builder.info
  return ds
  ```

  If you'd like NumPy arrays instead of `tf.data.Dataset`s or `tf.Tensor`s,
  you can pass the return value to `tfds.as_numpy`.

  Callers must pass arguments as keyword arguments.

  **Warning**: calling this function might potentially trigger the download
  of hundreds of GiB to disk. Refer to the `download` argument.

  Args:
    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`, set to > 1 to get batches of 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.
    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.
    with_info: `bool`, if True, tfds.load will return the tuple
      (tf.data.Dataset, tfds.core.DatasetInfo) containing the info associated
      with the builder.
    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`. `split` will be passed through by
      default. Example: `{'shuffle_files': True}`.
      Note that shuffle_files is False by default unless
      `split == tfds.Split.TRAIN`.
    try_gcs: `bool`, if True, tfds.load will see if the dataset exists on
      the public GCS bucket before building it locally.

  Returns:
    ds: `tf.data.Dataset`, the dataset requested, or if `split` is None, a
      `dict<key: tfds.Split, value: tfds.data.Dataset>`. If `batch_size=-1`,
      these will be full datasets as `tf.Tensor`s.
    ds_info: `tfds.core.DatasetInfo`, if `with_info` is True, then `tfds.load`
      will return a tuple `(ds, ds_info)` containing dataset information
      (version, features, splits, num_examples,...). Note that the `ds_info`
      object documents the entire dataset, regardless of the `split` requested.
      Split-specific information is available in `ds_info.splits`.