resolving documentation warnings (#833)

* add more underline

* fix LightningMudule import error

* remove unneeded blank line

* escape asterisk to fix inline emphasis warning

* add PULL_REQUEST_TEMPLATE.md

* add __init__.py and import imagenet_example

* fix duplicate label

* add noindex option to fix duplicate object warnings

* remove unexpected indent

* refer explicit LightningModule

* fix minor bug

* refer EarlyStopping explicitly

* restore exclude patterns

* change the way how to refer class

* remove unused import

* update badges & drop Travis/Appveyor (#826)

* drop Travis

* drop Appveyor

* update badges

* fix missing PyPI images & CI badges (#853)

* docs - anchor links (#848)

* docs - add links

* add desc.

* add Greeting action (#843)

* add Greeting action

* Update greetings.yml

Co-authored-by: William Falcon <waf2107@columbia.edu>

* add pep8speaks (#842)

* advanced profiler describe + cleaned up tests (#837)

* add py36 compatibility

* add test case to capture previous bug

* clean up tests

* clean up tests

* Update lightning_module_template.py

* Update lightning.py

* respond lint issues

* break long line

* break more lines

* checkout conflicting files from master

* shorten url

* checkout from upstream/master

* remove trailing whitespaces

* remove unused import LightningModule

* fix sphinx bot warnings

* Apply suggestions from code review

just to trigger CI

* Update .github/workflows/greetings.yml

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: William Falcon <waf2107@columbia.edu>
Co-authored-by: Jeremy Jordan <13970565+jeremyjordan@users.noreply.github.com>

docs/source/optimizers.rst

@@ -53,9 +53,9 @@ Lightning will call each optimizer sequentially:
 
 
 Step optimizers at arbitrary intervals
--------------------------------------
+----------------------------------------
 To do more interesting things with your optimizers such as learning rate warm-up or odd scheduling,
-override the :meth:`optimizer_step' function.
+override the :meth:`optimizer_step` function.
 
 For example, here step optimizer A every 2 batches and optimizer B every 4 batches
 
@@ -96,4 +96,4 @@ Here we add a learning-rate warm up
 
         # update params
         optimizer.step()
-        optimizer.zero_grad()
+        optimizer.zero_grad()