coverage increase (Lightning-AI#1167)

* fixed docs

* Docs (Lightning-AI#1164)

* fixed docs

* fixed docs

* fixed docs

* fixing Win failed import (Lightning-AI#1163)

* version

* try fix distrib

* update try import

* fixed docs

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

pytorch_lightning/utilities/debugging.py

@@ -1,78 +1,2 @@
-"""
-These flags are useful to help debug a model.
-
-Fast dev run
-------------
-
-This flag is meant for debugging a full train/val/test loop.
- It'll activate callbacks, everything but only with 1 training and 1 validation batch.
- Use this to debug a full run of your program quickly
-
-.. code-block:: python
-
-    # DEFAULT
-    trainer = Trainer(fast_dev_run=False)
-
-
-Inspect gradient norms
-----------------------
-
-Looking at grad norms can help you figure out where training might be going wrong.
-
-.. code-block:: python
-
-    # DEFAULT (-1 doesn't track norms)
-    trainer = Trainer(track_grad_norm=-1)
-
-    # track the LP norm (P=2 here)
-    trainer = Trainer(track_grad_norm=2)
-
-
-Make model overfit on subset of data
-------------------------------------
-
-A useful debugging trick is to make your model overfit a tiny fraction of the data.
-
-setting `overfit_pct > 0` will overwrite train_percent_check, val_percent_check, test_percent_check
-
-.. code-block:: python
-
-    # DEFAULT don't overfit (ie: normal training)
-    trainer = Trainer(overfit_pct=0.0)
-
-    # overfit on 1% of data
-    trainer = Trainer(overfit_pct=0.01)
-
-
-Print the parameter count by layer
-----------------------------------
-
-By default lightning prints a list of parameters *and submodules* when it starts training.
-
-.. code-block:: python
-
-    # DEFAULT print a full list of all submodules and their parameters.
-    trainer = Trainer(weights_summary='full')
-
-    # only print the top-level modules (i.e. the children of LightningModule).
-    trainer = Trainer(weights_summary='top')
-
-Print which gradients are nan
------------------------------
-
-This option prints a list of tensors with nan gradients::
-
-    # DEFAULT
-    trainer = Trainer(print_nan_grads=False)
-
-Log GPU usage
--------------
-
-Lightning automatically logs gpu usage to the test tube logs.
- It'll only do it at the metric logging interval, so it doesn't slow down training.
-
-"""
-
-
 class MisconfigurationException(Exception):
     pass