Automated documentation update.

PiperOrigin-RevId: 557571934

docs/catalog/celeb_a.md

@@ -3,7 +3,7 @@
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
   <meta itemprop="name" content="celeb_a" />
-  <meta itemprop="description" content="CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset&#10;with more than 200K celebrity images, each with 40 attribute annotations. The&#10;images in this dataset cover large pose variations and background clutter.&#10;CelebA has large diversities, large quantities, and rich annotations,&#10;including - 10,177 number of identities, - 202,599 number of face images, and -&#10;5 landmark locations, 40 binary attributes annotations per image.&#10;&#10;The dataset can be employed as the training and test sets for the following&#10;computer vision tasks: face attribute recognition, face detection, and landmark&#10;(or facial part) localization.&#10;&#10;Note: CelebA dataset may contain potential bias. The fairness indicators&#10;[example](https://www.tensorflow.org/responsible_ai/fairness_indicators/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study)&#10;goes into detail about several considerations to keep in mind while using the&#10;CelebA dataset.&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;celeb_a&#x27;, split=&#x27;train&#x27;)&#10;for ex in ds.take(4):&#10;  print(ex)&#10;```&#10;&#10;See [the guide](https://www.tensorflow.org/datasets/overview) for more&#10;informations on [tensorflow_datasets](https://www.tensorflow.org/datasets).&#10;&#10;&lt;img src=&quot;https://storage.googleapis.com/tfds-data/visualization/fig/celeb_a-2.0.1.png&quot; alt=&quot;Visualization&quot; width=&quot;500px&quot;&gt;&#10;&#10;" />
+  <meta itemprop="description" content="CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset&#10;with more than 200K celebrity images, each with 40 attribute annotations. The&#10;images in this dataset cover large pose variations and background clutter.&#10;CelebA has large diversities, large quantities, and rich annotations,&#10;including - 10,177 number of identities, - 202,599 number of face images, and -&#10;5 landmark locations, 40 binary attributes annotations per image.&#10;&#10;The dataset can be employed as the training and test sets for the following&#10;computer vision tasks: face attribute recognition, face detection, and landmark&#10;(or facial part) localization.&#10;&#10;Note: CelebA dataset may contain potential bias. The fairness indicators&#10;[example](https://www.tensorflow.org/responsible_ai/fairness_indicators/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study)&#10;goes into detail about several considerations to keep in mind while using the&#10;CelebA dataset.&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;celeb_a&#x27;, split=&#x27;train&#x27;)&#10;for ex in ds.take(4):&#10;  print(ex)&#10;```&#10;&#10;See [the guide](https://www.tensorflow.org/datasets/overview) for more&#10;informations on [tensorflow_datasets](https://www.tensorflow.org/datasets).&#10;&#10;" />
   <meta itemprop="url" content="https://www.tensorflow.org/datasets/catalog/celeb_a" />
   <meta itemprop="sameAs" content="http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html" />
   <meta itemprop="citation" content="@inproceedings{conf/iccv/LiuLWT15,&#10;  added-at = {2018-10-09T00:00:00.000+0200},&#10;  author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},&#10;  biburl = {https://www.bibsonomy.org/bibtex/250e4959be61db325d2f02c1d8cd7bfbb/dblp},&#10;  booktitle = {ICCV},&#10;  crossref = {conf/iccv/2015},&#10;  ee = {http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.425},&#10;  interhash = {3f735aaa11957e73914bbe2ca9d5e702},&#10;  intrahash = {50e4959be61db325d2f02c1d8cd7bfbb},&#10;  isbn = {978-1-4673-8391-2},&#10;  keywords = {dblp},&#10;  pages = {3730-3738},&#10;  publisher = {IEEE Computer Society},&#10;  timestamp = {2018-10-11T11:43:28.000+0200},&#10;  title = {Deep Learning Face Attributes in the Wild.},&#10;  url = {http://dblp.uni-trier.de/db/conf/iccv/iccv2015.html#LiuLWT15},&#10;  year = 2015&#10;}" />
@@ -12,6 +12,11 @@
 # `celeb_a`
 
 
+Note: This dataset has been updated since the last stable release. The new
+versions and config marked with
+<span class="material-icons" title="Available only in the tfds-nightly package">nights_stay</span>
+are only available in the `tfds-nightly` package.
+
 *   **Visualization**:
     <a class="button button-with-icon" href="https://knowyourdata-tfds.withgoogle.com/#tab=STATS&dataset=celeb_a">
     Explore in Know Your Data
@@ -50,13 +55,14 @@ CelebA dataset.
 
 *   **Versions**:
 
-    *   `2.0.0`: No release notes.
-    *   **`2.0.1`** (default): New split API
-        (https://tensorflow.org/datasets/splits)
+    *   `2.0.1`: New split API (https://tensorflow.org/datasets/splits)
+    *   **`2.1.0`** (default)
+        <span class="material-icons" title="Available only in the tfds-nightly package">nights_stay</span>:
+        Identity feature added.
 
-*   **Download size**: `1.38 GiB`
+*   **Download size**: `1.39 GiB`
 
-*   **Dataset size**: `1.62 GiB`
+*   **Dataset size**: `1.63 GiB`
 
 *   **Auto-cached**
     ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
@@ -204,45 +210,11 @@ FeaturesDict({
 
 *   **Figure**
     ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):
-
-<img src="https://storage.googleapis.com/tfds-data/visualization/fig/celeb_a-2.0.1.png" alt="Visualization" width="500px">
+    Not supported.
 
 *   **Examples**
     ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):
-
-<!-- mdformat off(HTML should not be auto-formatted) -->
-
-{% framebox %}
-
-<button id="displaydataframe">Display examples...</button>
-<div id="dataframecontent" style="overflow-x:auto"></div>
-<script>
-const url = "https://storage.googleapis.com/tfds-data/visualization/dataframe/celeb_a-2.0.1.html";
-const dataButton = document.getElementById('displaydataframe');
-dataButton.addEventListener('click', async () => {
-  // Disable the button after clicking (dataframe loaded only once).
-  dataButton.disabled = true;
-
-  const contentPane = document.getElementById('dataframecontent');
-  try {
-    const response = await fetch(url);
-    // Error response codes don't throw an error, so force an error to show
-    // the error message.
-    if (!response.ok) throw Error(response.statusText);
-
-    const data = await response.text();
-    contentPane.innerHTML = data;
-  } catch (e) {
-    contentPane.innerHTML =
-        'Error loading examples. If the error persist, please open '
-        + 'a new issue.';
-  }
-});
-</script>
-
-{% endframebox %}
-
-<!-- mdformat on -->
+    Missing.
 
 *   **Citation**: