[docs] Drop pl.metrics and define classes with explicit superclass (#231)

* Update docs

* Fix doctest

* Add docs/source/generated/ to gitignore

.gitignore

@@ -57,6 +57,7 @@ coverage.xml
 # Sphinx documentation
 docs/_build/
 docs/source/api/
+docs/source/generated/
 docs/source/*.md
 
 # PyBuilder

docs/source/pages/lightning.rst

@@ -19,11 +19,13 @@ While TorchMetrics was built to be used with native PyTorch, using TorchMetrics
 
 The example below shows how to use a metric in your `LightningModule <https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html>`_:
 
-.. code-block:: python
+.. testcode:: python
+
+    class MyModel(LightningModule):
 
         def __init__(self):
             ...
-        self.accuracy = pl.metrics.Accuracy()
+            self.accuracy = torchmetrics.Accuracy()
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -56,12 +58,14 @@ If ``on_epoch`` is True, the logger automatically logs the end of epoch metric v
     or reduction functions.
 
 
-.. code-block:: python
+.. testcode:: python
+
+    class MyModule(LightningModule):
 
         def __init__(self):
             ...
-        self.train_acc = pl.metrics.Accuracy()
-        self.valid_acc = pl.metrics.Accuracy()
+            self.train_acc = torchmetrics.Accuracy()
+            self.valid_acc = torchmetrics.Accuracy()
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -85,6 +89,8 @@ If ``on_epoch`` is True, the logger automatically logs the end of epoch metric v
 
     .. testcode:: python
 
+        class MyModule(LightningModule):
+
             def training_step(self, batch, batch_idx):
                 data, target = batch
                 preds = self(data)

docs/source/pages/overview.rst

@@ -1,3 +1,7 @@
+.. testsetup:: *
+
+    import torch
+    from pytorch_lightning.core.lightning import LightningModule
 
 ########
 Overview
@@ -91,7 +95,7 @@ the native `MetricCollection`_ module can also be used to wrap multiple metrics.
 
     from torchmetrics import Accuracy, MetricCollection
 
-    class MyModule():
+    class MyModule(torch.nn.Module):
         def __init__(self):
             ...
             # valid ways metrics will be identified as child modules
@@ -238,7 +242,7 @@ inside your LightningModule
 
     from torchmetrics import Accuracy, MetricCollection, Precision, Recall
 
-    class MyModule():
+    class MyModule(LightningModule):
         def __init__(self):
             metrics = MetricCollection([Accuracy(), Precision(), Recall()])
             self.train_metrics = metrics.clone(prefix='train_')