Ensure files end with a newline

.devcontainer/noop.txt

@@ -1,3 +1,3 @@
 This file is copied into the container along with environment.yml* from the
 parent folder. This is done to prevent the Dockerfile COPY instruction from 
-failing if no environment.yml is found.
+failing if no environment.yml is found.

SECURITY.md

@@ -38,4 +38,4 @@ We prefer all communications to be in English.
 
 Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).
 
-<!-- END MICROSOFT SECURITY.MD BLOCK -->
+<!-- END MICROSOFT SECURITY.MD BLOCK -->

docs/source/authentication.md

@@ -58,4 +58,3 @@ that ID:
 1. Via the search bar, find "Azure Active Directory" and open it.
 1. In the overview of that, you will see a field "Tenant ID"
 1. Create an environment variable called `HIML_TENANT_ID`, and set that to the tenant ID you just saw.
-

docs/source/downloading.md

@@ -84,4 +84,4 @@ Note that the path to local data must be a folder, not a single path. The folder
     following paths uploaded to your Datastore: ["baz/1.txt", "baz/2.txt"]
 
 This function takes additional parameters "overwrite" and "show_progress". If True, overwrite will overwrite any existing remote files with the same path. If False and there is a duplicate file, it will skip this file.
-If show_progress is set to True, the progress of the file upload will be visible in the terminal. 
+If show_progress is set to True, the progress of the file upload will be visible in the terminal. 

docs/source/examples/9/tensorboard_env.yml

@@ -7,4 +7,4 @@ dependencies:
   - python=3.7.3
   - pytorch=1.4.0
   - pip:
-    - tensorboard==2.2.1
+    - tensorboard==2.2.1

docs/source/runner.md

@@ -194,4 +194,3 @@ class MyContainer(LightningContainer):
 To the optimizer and LR scheduler: the Lightning model returned by `create_model` should define its own
 `configure_optimizers` method, with the same signature as `LightningModule.configure_optimizers`, 
 and returns a tuple containing the Optimizer and LRScheduler objects
-

docs/source/self_supervised_models.md

@@ -196,4 +196,4 @@ or for a local run
 
 ```
 python ML/runner.py --model=CXRImageClassifier --local_weights_path={LOCAL_PATH_TO_YOUR_SSL_CHECKPOINT}
-```
+```

docs/source/whole_slide_images.md

@@ -99,4 +99,3 @@ Test code created tiles of size 224x224 pilfes, loaded the mask images, and used
 For cuCIM the total time was 4.7s, 2.48s to retain the tiles as a Numpy stack but not save them as pngs. cuCIM has the option of cacheing images, but is actually made performance slightly worse, possibly because the natural tile sizes in the original tiffs were larger than the tile sizes.
 
 For OpenSlide the comparable total times were 5.7s, and 3.26s.
-

hi-ml-azure/.flake8

@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 120
 max-complexity = 25
-ignore = E731
+ignore = E731

hi-ml-azure/testazure/testazure/test_data/simple/hello_world_template.txt

@@ -60,4 +60,3 @@ def main() -> None:
 
 if __name__ == "__main__":
     main()
-

hi-ml-histopathology/.flake8

@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 120
 max-complexity = 25
-ignore = E731
+ignore = E731

hi-ml-histopathology/pytest.ini

@@ -7,4 +7,4 @@ addopts = --strict-markers
 markers =
     fast: Tests that should run very fast, and can act as smoke tests to see if something goes terribly wrong.
     gpu: Tests that should be executed both on a normal machine and on a machine with 2 GPUs.
-    flaky: Tests will automatically rerun if they fail.
+    flaky: Tests will automatically rerun if they fail.

hi-ml/.flake8

@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 120
 max-complexity = 25
-ignore = E731
+ignore = E731

hi-ml/other/image_loading/environment.yml

@@ -8,4 +8,4 @@ dependencies:
       - line_profiler==3.3.1
       - monai[all]==0.7.0
       - opencv-python-headless==4.5.4.58
-      - simpleitk==1.2.4
+      - simpleitk==1.2.4