cleanup: fix all remaining trailing whitespace

README.md

@@ -7,15 +7,15 @@ This README gives an overview of key concepts in TensorBoard, as well as how to
 interpret the visualizations TensorBoard provides. For an in-depth example of
 using TensorBoard, see the tutorial: [TensorBoard: Visualizing
 Learning][].
-For in-depth information on the Graph Visualizer, see this tutorial: 
+For in-depth information on the Graph Visualizer, see this tutorial:
 [TensorBoard: Graph Visualization][].
 
 [TensorBoard: Visualizing Learning]: https://www.tensorflow.org/get_started/summaries_and_tensorboard
 [TensorBoard: Graph Visualization]: https://www.tensorflow.org/get_started/graph_viz
 
 You may also want to watch
 [this video tutorial][] that walks
-through setting up and using TensorBoard. There's an associated 
+through setting up and using TensorBoard. There's an associated
 [tutorial with an end-to-end example of training TensorFlow and using TensorBoard][].
 
 [this video tutorial]: https://www.youtube.com/watch?v=eBbEDRsCmv4
@@ -33,7 +33,7 @@ directory by creating a summary writer:
 file_writer = tf.summary.FileWriter('/path/to/logs', sess.graph)
 ```
 
-For more details, see 
+For more details, see
 [the TensorBoard tutorial](https://www.tensorflow.org/get_started/summaries_and_tensorboard).
 Once you have event files, run TensorBoard and provide the log directory. If
 you're using a precompiled TensorFlow package (e.g. you installed via pip), run:
@@ -66,7 +66,7 @@ work, but there may be bugs or performance issues.
 ### Summary Ops: How TensorBoard gets data from TensorFlow
 
 The first step in using TensorBoard is acquiring data from your TensorFlow run.
-For this, you need 
+For this, you need
 [summary ops](https://www.tensorflow.org/api_docs/python/tf/summary).
 Summary ops are ops, like
 [`tf.matmul`](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/matmul)

docs/r1/overview.md

@@ -6,11 +6,11 @@ TensorFlow runs and graphs.
 This overview covers the key concepts in TensorBoard, as well as how to
 interpret the visualizations TensorBoard provides. For an in-depth example of
 using TensorBoard, see the [summaries guide][summaries.md].
-For in-depth information on the Graph Visualizer, see the [graphs guide](graphs.md). 
+For in-depth information on the Graph Visualizer, see the [graphs guide](graphs.md).
 
 You may also want to watch
 [this video tutorial][] that walks
-through setting up and using TensorBoard. There's an associated 
+through setting up and using TensorBoard. There's an associated
 [tutorial with an end-to-end example of training TensorFlow and using TensorBoard][].
 
 [this video tutorial]: https://www.youtube.com/watch?v=eBbEDRsCmv4
@@ -28,7 +28,7 @@ directory by creating a summary writer:
 file_writer = tf.summary.FileWriter('/path/to/logs', sess.graph)
 ```
 
-For more details, see 
+For more details, see
 [the TensorBoard tutorial](https://www.tensorflow.org/get_started/summaries_and_tensorboard).
 Once you have event files, run TensorBoard and provide the log directory. If
 you're using a precompiled TensorFlow package (e.g. you installed via pip), run:
@@ -61,7 +61,7 @@ work, but there may be bugs or performance issues.
 ### Summary Ops: How TensorBoard gets data from TensorFlow
 
 The first step in using TensorBoard is acquiring data from your TensorFlow run.
-For this, you need 
+For this, you need
 [summary ops](https://www.tensorflow.org/api_docs/python/tf/summary).
 Summary ops are ops, like
 [`tf.matmul`](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/matmul)

tensorboard/components/tf_color_scale/palettes.ts

@@ -59,9 +59,9 @@ export const palettes = {
   // (https://personal.sron.nl/~pault/).
   tensorboardColorBlindAssist: [
     '#ff7043',  // orange
-    '#0077bb',  // blue    
+    '#0077bb',  // blue
     '#cc3311',  // red
-    '#33bbee',  // cyan    
+    '#33bbee',  // cyan
     '#ee3377',  // magenta
     '#009988',  // teal
     '#bbbbbb',  // grey

tensorboard/plugins/custom_scalar/tf_custom_scalar_dashboard/tf-custom-scalar-dashboard.html

@@ -317,8 +317,8 @@ <h3>The custom scalars dashboard is inactive.</h3>
         this._requestManager.request(url).then(handlePluginsListingResponse);
       },
       _reloadCharts() {
-        const charts = 
-          this.querySelectorAll('tf-custom-scalar-margin-chart-card, ' + 
+        const charts =
+          this.querySelectorAll('tf-custom-scalar-margin-chart-card, ' +
                                 'tf-custom-scalar-multi-line-chart-card');
         charts.forEach(chart => {
           chart.reload();

tensorboard/plugins/debugger/tf_debugger_dashboard/tf-debugger-resizer.html

@@ -58,7 +58,7 @@
       transform: translate(-50%, -50%);
     }
 
-    /** This block prevents the bars rotator from having a height that is 
+    /** This block prevents the bars rotator from having a height that is
         the entire viewport, thus occluding it and giving it an undesired cursor
         value. */
     .bars-rotator {

tensorboard/plugins/graph/tf_graph_common/edge.ts

@@ -291,7 +291,7 @@ export function appendEdge(edgeGroup, d: EdgeData,
   // Give the path a unique id, which will be used to link
   // the textPath (edge label) to this path.
   let pathId = 'path_' + getEdgeKey(d);
-  
+
   let strokeWidth;
   if (sceneElement.renderHierarchy.edgeWidthFunction) {
     // Compute edge thickness based on the user-specified method.

tensorboard/plugins/hparams/api.d.ts

@@ -17,21 +17,21 @@ limitations under the License.
  * Contains TypeScript type definitions for the JSON-representation of the
  * protocol buffers defined in api.proto
  *
- * TODO(erez): Add the rest of the definitions once these are needed. 
+ * TODO(erez): Add the rest of the definitions once these are needed.
  */
 declare namespace tf.hparams {
   export interface MetricName {
     group: string,
     tag: string,
   }
-  
+
   export interface MetricValue {
     name: MetricName,
     value: number,
     trainingStep: number,
     wallTimeSecs: number,
   }
-  
+
   export interface SessionGroup {
     name: string,
     hparams: {

tensorboard/plugins/hparams/http_api.md

@@ -3,12 +3,12 @@ HParams backend supports calling requests with both HTTP POST and GET.
 Each method below has an associated 'request' protocol buffer and returns
 a 'response' protocol buffer. The 'request' is expected to be encoded in JSON
 format and the response will be returned encoded in JSON format. Encoding protos
-to JSON format is described 
+to JSON format is described
 [here](https://developers.google.com/protocol-buffers/docs/proto3#json)).
 
 For POST the request is expected to be passed in the request body. For GET the
-request is expected to be passed (properly escaped) in a URL query parameter 
-named 'request'. 
+request is expected to be passed (properly escaped) in a URL query parameter
+named 'request'.
 
 ### Notes:
 1. Per Tensorboard's conventions, each end-point has a "data/plugin/hparams"

tensorboard/plugins/hparams/tf_hparams_google_analytics_tracker/test/tf-hparams-google-analytics-tracker-test.html

@@ -18,7 +18,7 @@
      tf-hparams-google-analytics-tracker is an element with no-op methods
      and thus doesn't need tests. We keep this file here instead of deleting
      it entirely, so that we don't need to change the sites where its used
-     as well, when syncing the code back to Google's internal repo. 
+     as well, when syncing the code back to Google's internal repo.
   -->
 <html>
   <head>

tensorboard/plugins/hparams/tf_hparams_parallel_coords_plot/tf-hparams-parallel-coords-plot.html

@@ -201,7 +201,7 @@
         }
         const configuration = this.options.configuration;
         // See if we need to redraw from scratch. We redraw from scratch if
-        // this is initialization or if configuration.schema has changed. 
+        // this is initialization or if configuration.schema has changed.
         if (this._interactionManager === undefined ||
             !_.isEqual(this._interactionManager.schema(),
                        configuration.schema)) {

tensorboard/plugins/hparams/tf_hparams_sessions_pane/tf-hparams-sessions-pane.html

@@ -166,7 +166,7 @@
         // The URL to use for the bug-report button.
         bugReportUrl: String,
         experimentName: String,
-        // See the comments in tf-hparams-query-pane.html for details. 
+        // See the comments in tf-hparams-query-pane.html for details.
         configuration: Object,
         sessionGroups: Array,
 

tensorboard/plugins/interactive_inference/witwidget/pip_package/BUILD

@@ -25,4 +25,4 @@ sh_binary(
         "local",
         "manual",
     ],
-) 
+)

tensorboard/plugins/interactive_inference/witwidget/pip_package/RELEASE.md

@@ -1,5 +1,5 @@
 # To release a new version of witwidget on PyPI and NPM:
- 
+
 1. Ensure updated version numbers have been merged to the master branch in
 tensorboard/plugins/interactive_inference/witwidget/version.py
 and tensorboard/plugins/interactive_inference/witwidget/notebook/jupyter/js/package.json

tensorboard/plugins/mesh/BUILD

@@ -32,7 +32,7 @@ py_test(
     srcs = ["metadata_test.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":metadata",        
+        ":metadata",
         "//tensorboard:expect_tensorflow_installed",
         "//tensorboard/util:test_util",
         "@org_pythonhosted_mock",
@@ -48,9 +48,9 @@ py_library(
     deps = [
         ":metadata",
         ":protos_all_py_pb2",
-        "//tensorboard:expect_numpy_installed",        
+        "//tensorboard:expect_numpy_installed",
         "//tensorboard:plugin_util",
-        "//tensorboard/backend:http_util",        
+        "//tensorboard/backend:http_util",
         "//tensorboard/plugins:base_plugin",
         "//tensorboard/util:tensor_util",
         "@org_pythonhosted_six",
@@ -65,7 +65,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":summary",
-        "//tensorboard:expect_tensorflow_installed",        
+        "//tensorboard:expect_tensorflow_installed",
         "//tensorboard/backend:application",
         "//tensorboard/backend/event_processing:event_multiplexer",
         "@org_pocoo_werkzeug",
@@ -81,8 +81,8 @@ py_test(
         ":mesh_plugin",
         ":summary",
         ":test_utils",
-        "//tensorboard:expect_numpy_installed",        
-        "//tensorboard:expect_tensorflow_installed",        
+        "//tensorboard:expect_numpy_installed",
+        "//tensorboard:expect_tensorflow_installed",
         "//tensorboard/backend:application",
         "//tensorboard/backend/event_processing:event_multiplexer",
         "//tensorboard/plugins:base_plugin",
@@ -114,7 +114,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":summary",
-        ":test_utils",        
+        ":test_utils",
         "//tensorboard:expect_tensorflow_installed",
         "//tensorboard/util:test_util",
     ],

tensorboard/plugins/mesh/README.md

@@ -2,8 +2,8 @@
 
 ## Overview
 
-Meshes and point clouds are important and powerful types of data to represent 
-3D shapes and widely studied in the field of computer vision and computer 
+Meshes and point clouds are important and powerful types of data to represent
+3D shapes and widely studied in the field of computer vision and computer
 graphics.
 
 3D data is becoming more ubiquitous and researchers attack new problems like
@@ -22,8 +22,8 @@ interact with the rendered objects.
 ## Summary API
 
 Meshes and point clouds can be represented by a set of tensors. For
-example, one can view a point cloud as a set of 3D coordinates of the points 
-and some colors associated with each point. Check out a simple example with 
+example, one can view a point cloud as a set of 3D coordinates of the points
+and some colors associated with each point. Check out a simple example with
 static mesh:
 
 ```python
@@ -36,7 +36,7 @@ point_colors = tf.constant([[[128, 104, 227], ...]], shape=[1, 1064, 3])
 summary = mesh_summary.op('point_cloud', vertices=point_cloud, colors=point_colors)
 ```
 
-**NOTE:** The `colors` tensor is optional in this case but can be useful to 
+**NOTE:** The `colors` tensor is optional in this case but can be useful to
 show different semantics of the points.
 
 A mesh can be represented by a point cloud together with a set of faces,
@@ -89,7 +89,7 @@ camera_config = {
 ...
 ```
 
-Keep in mind that scene configuration is not a trainable variable (i.e., it is 
+Keep in mind that scene configuration is not a trainable variable (i.e., it is
 a static attribute) and is fixed at summary-creation time.
 
 ## Example
@@ -107,7 +107,7 @@ bazel run tensorboard/plugins/mesh:mesh_demo -- --mesh_path=path/to/ply/file
 
 ## How to install
 
-The mesh plugin isn’t yet part of stable TensorBoard, so you’ll need to 
+The mesh plugin isn’t yet part of stable TensorBoard, so you’ll need to
 install the latest TensorBoard nightly build to use it.
 
 ### Colab

tensorboard/plugins/mesh/http_api.md

@@ -18,49 +18,49 @@ Here is an example:
         "train_run": {
             "mesh_color_tensor": {
                 "samples": 1
-            }, 
+            },
             "point_cloud": {
                 "samples": 1
             }
         }
     }
 ```
-Note that runs without any mesh tags are included as keys with value the empty 
+Note that runs without any mesh tags are included as keys with value the empty
 dictionary.
 
 
 ## `/data/plugin/mesh/meshes?tag=mesh_color_tensor&run=train_run&sample=0`
 
 Retrieves all necessary metadata to render a mesh with particular tag.
 
-Returns list of metadata for each data (tensor) that should be retrieved next. 
-This includes content type (i.e. vertices, faces or colors), shape of the 
-data, scene configuration, wall time etc. Type of the content maps directly to 
-underlying binary data type, i.e. `float32`, `int32` or `uint8`. Content type 
-mapping to their enum constant representations is given by a 
+Returns list of metadata for each data (tensor) that should be retrieved next.
+This includes content type (i.e. vertices, faces or colors), shape of the
+data, scene configuration, wall time etc. Type of the content maps directly to
+underlying binary data type, i.e. `float32`, `int32` or `uint8`. Content type
+mapping to their enum constant representations is given by a
 [proto definition](https://github.com/tensorflow/tensorboard/plugins/mesh/plugin_data.proto).
 
 Here is an example:
 ```json
     [
-        {  
-            "config": "{\"camera\": {\"cls\": \"PerspectiveCamera\", \"fov\": 
+        {
+            "config": "{\"camera\": {\"cls\": \"PerspectiveCamera\", \"fov\":
             75}}",
             "data_shape": [1, 17192, 3],
             "step": 0,
             "content_type": 2,
             "wall_time": 1556678491.836787
         },
-        {  
-            "config": "{\"camera\": {\"cls\": \"PerspectiveCamera\", \"fov\": 
+        {
+            "config": "{\"camera\": {\"cls\": \"PerspectiveCamera\", \"fov\":
             75}}",
             "data_shape": [1, 9771, 3],
             "step": 0,
             "content_type": 3,
             "wall_time": 1556678491.836787
         },
-        {  
-            "config": "{\"camera\": {\"cls\": \"PerspectiveCamera\", \"fov\": 
+        {
+            "config": "{\"camera\": {\"cls\": \"PerspectiveCamera\", \"fov\":
             75}}",
             "data_shape": [1, 9771, 3],
             "step": 0,
@@ -69,19 +69,19 @@ Here is an example:
         }
     ]
 ```
-Scene configuration is a JSON string passed to `config_dict` during summary 
-creation and may contain the following high-level keys: `camera`, `lights` and 
-`material`. Each such key must correspond to an object with `cls` property 
-which must be a valid THREE.js class. The rest of the keys of the object will 
-be used as parameters to the class constructor and should also be valid 
+Scene configuration is a JSON string passed to `config_dict` during summary
+creation and may contain the following high-level keys: `camera`, `lights` and
+`material`. Each such key must correspond to an object with `cls` property
+which must be a valid THREE.js class. The rest of the keys of the object will
+be used as parameters to the class constructor and should also be valid
 THREE.js options. Invalid keys will be ignored by the library.
 
 
 ## `/data/plugin/mesh/data?tag=mesh_color_tensor&run=train_run&content_type=VERTEX&sample=0`
 
-Retrieves binary data of particular type representing some part of the mesh, 
+Retrieves binary data of particular type representing some part of the mesh,
 for example vertices with 3D coordinates.
 
-Returns stream of binary data, which will represent either mesh vertices, 
-faces or RGB colors. Response type of this request is set to `arraybuffer` 
+Returns stream of binary data, which will represent either mesh vertices,
+faces or RGB colors. Response type of this request is set to `arraybuffer`
 therefore Typed Array will be received instead of a JSON string.

tensorboard/plugins/mesh/mesh_plugin_test.py

@@ -41,7 +41,7 @@
   import mock  # pylint: disable=g-import-not-at-top,unused-import
 
 
-@tensorboard_test_util.run_v1_only('requires tf.Session') 
+@tensorboard_test_util.run_v1_only('requires tf.Session')
 class MeshPluginTest(tf.test.TestCase):
   """Tests for mesh plugin server."""