Automated documentation update

PiperOrigin-RevId: 283404735

Author: Conchylicultor

docs/catalog/abstract_reasoning.md

@@ -120,11 +120,9 @@ https://console.cloud.google.com/storage/browser/ravens-matrices Please put all
 the tar.gz files in manual_dir.
 
 ### Statistics
-
 None computed
 
 ### Features
-
 ```python
 FeaturesDict({
     'answers': Video(Image(shape=(160, 160, 1), dtype=tf.uint8)),
@@ -141,7 +139,6 @@ FeaturesDict({
 *   [https://github.com/deepmind/abstract-reasoning-matrices](https://github.com/deepmind/abstract-reasoning-matrices)
 
 ## `abstract_reasoning/interpolation`
-
 As in the neutral split, $S$ consisted of any \
 triples $[r, o, a]$. For interpolation, in the training set, when the \
 attribute was "colour" or "size" (i.e., the ordered attributes), the values of \
@@ -161,11 +158,9 @@ https://console.cloud.google.com/storage/browser/ravens-matrices Please put all
 the tar.gz files in manual_dir.
 
 ### Statistics
-
 None computed
 
 ### Features
-
 ```python
 FeaturesDict({
     'answers': Video(Image(shape=(160, 160, 1), dtype=tf.uint8)),
@@ -182,7 +177,6 @@ FeaturesDict({
 *   [https://github.com/deepmind/abstract-reasoning-matrices](https://github.com/deepmind/abstract-reasoning-matrices)
 
 ## `abstract_reasoning/extrapolation`
-
 Same as in interpolation, but the values of \
 the attributes were restricted to the lower half of the discrete set during \
 training, whereas in the test set they took values in the upper half.
@@ -217,7 +211,6 @@ FeaturesDict({
 *   [https://github.com/deepmind/abstract-reasoning-matrices](https://github.com/deepmind/abstract-reasoning-matrices)
 
 ## `abstract_reasoning/attr.rel.pairs`
-
 All $S$ contained at least two triples, \
 $([r_1,o_1,a_1],[r_2,o_2,a_2]) = (t_1, t_2)$, of which 400 are viable. We \
 randomly allocated 360 to the training set and 40 to the test set. Members \
@@ -255,7 +248,6 @@ FeaturesDict({
 *   [https://github.com/deepmind/abstract-reasoning-matrices](https://github.com/deepmind/abstract-reasoning-matrices)
 
 ## `abstract_reasoning/attr.rels`
-
 In our dataset, there are 29 possible unique \
 triples $[r,o,a]$. We allocated seven of these for the test set, at random, \
 but such that each of the attributes was represented exactly once in this set. \

docs/catalog/celeb_a_hq.md

@@ -2,14 +2,12 @@
   <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
-
   <meta itemprop="name" content="celeb_a_hq" />
   <meta itemprop="description" content="High-quality version of the CELEBA&#10;dataset, consisting of 30000 images in 1024 x 1024 resolution.&#10;&#10;WARNING: This dataset currently requires you to prepare images on your own.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('celeb_a_hq', split='train')&#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_hq" />
   <meta itemprop="sameAs" content="https://github.com/tkarras/progressive_growing_of_gans" />
   <meta itemprop="citation" content="@article{DBLP:journals/corr/abs-1710-10196,&#10;  author    = {Tero Karras and&#10;               Timo Aila and&#10;               Samuli Laine and&#10;               Jaakko Lehtinen},&#10;  title     = {Progressive Growing of GANs for Improved Quality, Stability, and Variation},&#10;  journal   = {CoRR},&#10;  volume    = {abs/1710.10196},&#10;  year      = {2017},&#10;  url       = {http://arxiv.org/abs/1710.10196},&#10;  archivePrefix = {arXiv},&#10;  eprint    = {1710.10196},&#10;  timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},&#10;  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1710-10196},&#10;  bibsource = {dblp computer science bibliography, https://dblp.org}&#10;}&#10;" />
 </div>
-
 # `celeb_a_hq` (Manual download)
 
 High-quality version of the CELEBA dataset, consisting of 30000 images in 1024 x