Update doc

PiperOrigin-RevId: 252698370

Author: Conchylicultor

docs/api_docs/python/tfds/core/BeamBasedBuilder.md

@@ -78,7 +78,7 @@ Callers must pass arguments as keyword arguments.
 ```python
 as_dataset(
     split=None,
-    batch_size=1,
+    batch_size=None,
     shuffle_files=None,
     as_supervised=False
 )
@@ -95,10 +95,10 @@ Callers must pass arguments as keyword arguments.
     which subset(s) of the data to read. If None (default), returns all splits
     in a dict `<key: tfds.Split, value: tf.data.Dataset>`.
 *   <b>`batch_size`</b>: `int`, batch size. Note that variable-length features
-    will be 0-padded if `batch_size > 1`. Users that want more custom behavior
-    should use `batch_size=1` and use the `tf.data` API to construct a custom
-    pipeline. If `batch_size == -1`, will return feature dictionaries of the
-    whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
+    will be 0-padded if `batch_size` is set. Users that want more custom
+    behavior should use `batch_size=None` and use the `tf.data` API to construct
+    a custom pipeline. If `batch_size == -1`, will return feature dictionaries
+    of the whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
 *   <b>`shuffle_files`</b>: `bool`, whether to shuffle the input files. Defaults
     to `True` if `split == tfds.Split.TRAIN` and `False` otherwise.
 *   <b>`as_supervised`</b>: `bool`, if `True`, the returned `tf.data.Dataset`

docs/api_docs/python/tfds/core/DatasetBuilder.md

@@ -109,7 +109,7 @@ Callers must pass arguments as keyword arguments.
 ```python
 as_dataset(
     split=None,
-    batch_size=1,
+    batch_size=None,
     shuffle_files=None,
     as_supervised=False
 )
@@ -126,10 +126,10 @@ Callers must pass arguments as keyword arguments.
     which subset(s) of the data to read. If None (default), returns all splits
     in a dict `<key: tfds.Split, value: tf.data.Dataset>`.
 *   <b>`batch_size`</b>: `int`, batch size. Note that variable-length features
-    will be 0-padded if `batch_size > 1`. Users that want more custom behavior
-    should use `batch_size=1` and use the `tf.data` API to construct a custom
-    pipeline. If `batch_size == -1`, will return feature dictionaries of the
-    whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
+    will be 0-padded if `batch_size` is set. Users that want more custom
+    behavior should use `batch_size=None` and use the `tf.data` API to construct
+    a custom pipeline. If `batch_size == -1`, will return feature dictionaries
+    of the whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
 *   <b>`shuffle_files`</b>: `bool`, whether to shuffle the input files. Defaults
     to `True` if `split == tfds.Split.TRAIN` and `False` otherwise.
 *   <b>`as_supervised`</b>: `bool`, if `True`, the returned `tf.data.Dataset`

docs/api_docs/python/tfds/core/GeneratorBasedBuilder.md

@@ -87,7 +87,7 @@ Callers must pass arguments as keyword arguments.
 ```python
 as_dataset(
     split=None,
-    batch_size=1,
+    batch_size=None,
     shuffle_files=None,
     as_supervised=False
 )
@@ -104,10 +104,10 @@ Callers must pass arguments as keyword arguments.
     which subset(s) of the data to read. If None (default), returns all splits
     in a dict `<key: tfds.Split, value: tf.data.Dataset>`.
 *   <b>`batch_size`</b>: `int`, batch size. Note that variable-length features
-    will be 0-padded if `batch_size > 1`. Users that want more custom behavior
-    should use `batch_size=1` and use the `tf.data` API to construct a custom
-    pipeline. If `batch_size == -1`, will return feature dictionaries of the
-    whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
+    will be 0-padded if `batch_size` is set. Users that want more custom
+    behavior should use `batch_size=None` and use the `tf.data` API to construct
+    a custom pipeline. If `batch_size == -1`, will return feature dictionaries
+    of the whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
 *   <b>`shuffle_files`</b>: `bool`, whether to shuffle the input files. Defaults
     to `True` if `split == tfds.Split.TRAIN` and `False` otherwise.
 *   <b>`as_supervised`</b>: `bool`, if `True`, the returned `tf.data.Dataset`

docs/api_docs/python/tfds/load.md

@@ -12,7 +12,7 @@ tfds.load(
     name,
     split=None,
     data_dir=None,
-    batch_size=1,
+    batch_size=None,
     download=True,
     as_supervised=False,
     with_info=False,
@@ -71,9 +71,9 @@ of hundreds of GiB to disk. Refer to the `download` argument.
     <a href="../tfds/Split.md#TEST"><code>tfds.Split.TEST</code></a>).
 *   <b>`data_dir`</b>: `str` (optional), directory to read/write data. Defaults
     datasets are stored.
-*   <b>`batch_size`</b>: `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.
+*   <b>`batch_size`</b>: `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.
 *   <b>`download`</b>: `bool` (optional), whether to call
     <a href="../tfds/core/DatasetBuilder.md#download_and_prepare"><code>tfds.core.DatasetBuilder.download_and_prepare</code></a>
     before calling `tf.DatasetBuilder.as_dataset`. If `False`, data is expected

docs/api_docs/python/tfds/testing/DummyDatasetSharedGenerator.md

@@ -82,7 +82,7 @@ Callers must pass arguments as keyword arguments.
 ```python
 as_dataset(
     split=None,
-    batch_size=1,
+    batch_size=None,
     shuffle_files=None,
     as_supervised=False
 )
@@ -99,10 +99,10 @@ Callers must pass arguments as keyword arguments.
     which subset(s) of the data to read. If None (default), returns all splits
     in a dict `<key: tfds.Split, value: tf.data.Dataset>`.
 *   <b>`batch_size`</b>: `int`, batch size. Note that variable-length features
-    will be 0-padded if `batch_size > 1`. Users that want more custom behavior
-    should use `batch_size=1` and use the `tf.data` API to construct a custom
-    pipeline. If `batch_size == -1`, will return feature dictionaries of the
-    whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
+    will be 0-padded if `batch_size` is set. Users that want more custom
+    behavior should use `batch_size=None` and use the `tf.data` API to construct
+    a custom pipeline. If `batch_size == -1`, will return feature dictionaries
+    of the whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
 *   <b>`shuffle_files`</b>: `bool`, whether to shuffle the input files. Defaults
     to `True` if `split == tfds.Split.TRAIN` and `False` otherwise.
 *   <b>`as_supervised`</b>: `bool`, if `True`, the returned `tf.data.Dataset`

docs/api_docs/python/tfds/testing/DummyMnist.md

@@ -62,7 +62,7 @@ __init__(
 ```python
 as_dataset(
     split=None,
-    batch_size=1,
+    batch_size=None,
     shuffle_files=None,
     as_supervised=False
 )
@@ -79,10 +79,10 @@ Callers must pass arguments as keyword arguments.
     which subset(s) of the data to read. If None (default), returns all splits
     in a dict `<key: tfds.Split, value: tf.data.Dataset>`.
 *   <b>`batch_size`</b>: `int`, batch size. Note that variable-length features
-    will be 0-padded if `batch_size > 1`. Users that want more custom behavior
-    should use `batch_size=1` and use the `tf.data` API to construct a custom
-    pipeline. If `batch_size == -1`, will return feature dictionaries of the
-    whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
+    will be 0-padded if `batch_size` is set. Users that want more custom
+    behavior should use `batch_size=None` and use the `tf.data` API to construct
+    a custom pipeline. If `batch_size == -1`, will return feature dictionaries
+    of the whole dataset with `tf.Tensor`s instead of a `tf.data.Dataset`.
 *   <b>`shuffle_files`</b>: `bool`, whether to shuffle the input files. Defaults
     to `True` if `split == tfds.Split.TRAIN` and `False` otherwise.
 *   <b>`as_supervised`</b>: `bool`, if `True`, the returned `tf.data.Dataset`