Automated documentation update

PiperOrigin-RevId: 280547812

Author: Conchylicultor

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

@@ -18,7 +18,6 @@ source</a>
 ## Class `RaggedConstant`
 
 <!-- Start diff -->
-
 Container of tf.ragged.constant values.
 
 <!-- Placeholder for "Used in" -->

docs/catalog/_toc.yaml

@@ -62,6 +62,8 @@ toc:
     title: deep_weeds
   - path: /datasets/catalog/diabetic_retinopathy_detection
     title: diabetic_retinopathy_detection
+  - path: /datasets/catalog/dmlab
+    title: dmlab
   - path: /datasets/catalog/downsampled_imagenet
     title: downsampled_imagenet
   - path: /datasets/catalog/dsprites
@@ -212,6 +214,8 @@ toc:
     title: imdb_reviews
   - path: /datasets/catalog/lm1b
     title: lm1b
+  - path: /datasets/catalog/math_dataset
+    title: math_dataset
   - path: /datasets/catalog/multi_nli
     title: multi_nli
   - path: /datasets/catalog/multi_nli_mismatch

docs/catalog/dmlab.md

@@ -0,0 +1,80 @@
+<div itemscope itemtype="http://schema.org/Dataset">
+  <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
+    <meta itemprop="name" content="TensorFlow Datasets" />
+  </div>
+
+  <meta itemprop="name" content="dmlab" />
+  <meta itemprop="description" content="&#10;        The Dmlab dataset contains frames observed by the agent acting in the&#10;        DeepMind Lab environment, which are annotated by the distance between&#10;        the agent and various objects present in the environment. The goal is to&#10;        is to evaluate the ability of a visual model to reason about distances&#10;        from the visual input in 3D environments. The Dmlab dataset consists of&#10;        360x480 color images in 6 classes. The classes are&#10;        {close, far, very far} x {positive reward, negative reward}&#10;        respectively.&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('dmlab', 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/dmlab" />
+  <meta itemprop="sameAs" content="https://github.com/google-research/task_adaptation" />
+  <meta itemprop="citation" content="@article{zhai2019visual,&#10;        title={The Visual Task Adaptation Benchmark},&#10;        author={Xiaohua Zhai and Joan Puigcerver and Alexander Kolesnikov and&#10;               Pierre Ruyssen and Carlos Riquelme and Mario Lucic and&#10;               Josip Djolonga and Andre Susano Pinto and Maxim Neumann and&#10;               Alexey Dosovitskiy and Lucas Beyer and Olivier Bachem and&#10;               Michael Tschannen and Marcin Michalski and Olivier Bousquet and&#10;               Sylvain Gelly and Neil Houlsby},&#10;                              year={2019},&#10;                              eprint={1910.04867},&#10;                              archivePrefix={arXiv},&#10;                              primaryClass={cs.CV},&#10;                              url = {https://arxiv.org/abs/1910.04867}&#10;                          }" />
+</div>
+
+# `dmlab`
+
+The Dmlab dataset contains frames observed by the agent acting in the DeepMind
+Lab environment, which are annotated by the distance between the agent and
+various objects present in the environment. The goal is to is to evaluate the
+ability of a visual model to reason about distances from the visual input in 3D
+environments. The Dmlab dataset consists of 360x480 color images in 6 classes.
+The classes are {close, far, very far} x {positive reward, negative reward}
+respectively.
+
+*   URL:
+    [https://github.com/google-research/task_adaptation](https://github.com/google-research/task_adaptation)
+*   `DatasetBuilder`:
+    [`tfds.image.dmlab.Dmlab`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/image/dmlab.py)
+*   Version: `v2.0.0`
+*   Versions:
+
+    *   **`2.0.0`** (default):
+
+*   Size: `2.81 GiB`
+
+## Features
+
+```python
+FeaturesDict({
+    'filename': Text(shape=(), dtype=tf.string),
+    'image': Image(shape=(360, 480, 3), dtype=tf.uint8),
+    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=6),
+})
+```
+
+## Statistics
+
+Split      | Examples
+:--------- | -------:
+ALL        | 110,913
+TRAIN      | 65,550
+TEST       | 22,735
+VALIDATION | 22,628
+
+## Homepage
+
+*   [https://github.com/google-research/task_adaptation](https://github.com/google-research/task_adaptation)
+
+## Supervised keys (for `as_supervised=True`)
+
+`(u'image', u'label')`
+
+## Citation
+
+```
+@article{zhai2019visual,
+        title={The Visual Task Adaptation Benchmark},
+        author={Xiaohua Zhai and Joan Puigcerver and Alexander Kolesnikov and
+               Pierre Ruyssen and Carlos Riquelme and Mario Lucic and
+               Josip Djolonga and Andre Susano Pinto and Maxim Neumann and
+               Alexey Dosovitskiy and Lucas Beyer and Olivier Bachem and
+               Michael Tschannen and Marcin Michalski and Olivier Bousquet and
+               Sylvain Gelly and Neil Houlsby},
+                              year={2019},
+                              eprint={1910.04867},
+                              archivePrefix={arXiv},
+                              primaryClass={cs.CV},
+                              url = {https://arxiv.org/abs/1910.04867}
+                          }
+```
+
+--------------------------------------------------------------------------------

docs/catalog/duke_ultrasound.md

@@ -2,12 +2,14 @@
   <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
+
   <meta itemprop="name" content="duke_ultrasound" />
   <meta itemprop="description" content="DukeUltrasound is an ultrasound dataset collected at Duke University with a &#10;Verasonics c52v probe. It contains delay-and-sum (DAS) beamformed data &#10;as well as data post-processed with Siemens Dynamic TCE for speckle &#10;reduction, contrast enhancement and improvement in conspicuity of &#10;anatomical structures. These data were collected with support from the&#10;National Institute of Biomedical Imaging and Bioengineering under Grant &#10;R01-EB026574 and National Institutes of Health under Grant 5T32GM007171-44.&#10;A usage example is avalible &#10;[here](https://colab.research.google.com/drive/1R_ARqpWoiHcUQWg1Fxwyx-ZkLi0IZ5qs).&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('duke_ultrasound', 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/duke_ultrasound" />
-  <meta itemprop="sameAs" content="https://arxiv.org/abs/1908.05782" />
+  <meta itemprop="sameAs" content="https://github.com/ouwen/mimicknet" />
   <meta itemprop="citation" content="@article{DBLP:journals/corr/abs-1908-05782,&#10;  author    = {Ouwen Huang and&#10;               Will Long and&#10;               Nick Bottenus and&#10;               Gregg E. Trahey and&#10;               Sina Farsiu and&#10;               Mark L. Palmeri},&#10;  title     = {MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box&#10;               Constraints},&#10;  journal   = {CoRR},&#10;  volume    = {abs/1908.05782},&#10;  year      = {2019},&#10;  url       = {http://arxiv.org/abs/1908.05782},&#10;  archivePrefix = {arXiv},&#10;  eprint    = {1908.05782},&#10;  timestamp = {Mon, 19 Aug 2019 13:21:03 +0200},&#10;  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1908-05782},&#10;  bibsource = {dblp computer science bibliography, https://dblp.org}&#10;}" />
 </div>
+
 # `duke_ultrasound`
 
 DukeUltrasound is an ultrasound dataset collected at Duke University with a
@@ -19,7 +21,8 @@ and Bioengineering under Grant R01-EB026574 and National Institutes of Health
 under Grant 5T32GM007171-44. A usage example is avalible
 [here](https://colab.research.google.com/drive/1R_ARqpWoiHcUQWg1Fxwyx-ZkLi0IZ5qs).
 
-*   URL: [https://arxiv.org/abs/1908.05782](https://arxiv.org/abs/1908.05782)
+*   URL:
+    [https://github.com/ouwen/mimicknet](https://github.com/ouwen/mimicknet)
 *   `DatasetBuilder`:
     [`tfds.image.duke_ultrasound.DukeUltrasound`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/image/duke_ultrasound.py)
 *   Version: `v1.0.0`
@@ -69,7 +72,7 @@ VALIDATION | 278
 
 ## Homepage
 
-*   [https://arxiv.org/abs/1908.05782](https://arxiv.org/abs/1908.05782)
+*   [https://github.com/ouwen/mimicknet](https://github.com/ouwen/mimicknet)
 
 ## Supervised keys (for `as_supervised=True`)
 `(u'das/dB', u'dtce')`