diff --git a/docs/source-pytorch/upgrade/sections/1_6_regular.rst b/docs/source-pytorch/upgrade/sections/1_6_regular.rst
index e0160845ad..e36d328698 100644
--- a/docs/source-pytorch/upgrade/sections/1_6_regular.rst
+++ b/docs/source-pytorch/upgrade/sections/1_6_regular.rst
@@ -15,7 +15,7 @@
      - `PR9699`_
 
    * - used Trainer’s flag ``checkpoint_callback``
-     - set ``enable_checkpointing``. If you set ``enable_checkpointing=True``, it configures a default ``ModelCheckpoint`` callback if none is provided ``lightning_pytorch.trainer.trainer.Trainer.callbacks.ModelCheckpoint``
+     - set ``enable_checkpointing``. If you set ``enable_checkpointing=True``, it configures a default ``ModelCheckpoint`` callback if none is provided ``lightning.pytorch.trainer.trainer.Trainer.callbacks.ModelCheckpoint``
      - `PR9754`_
 
    * - used Trainer’s flag ``stochastic_weight_avg``
diff --git a/docs/source-pytorch/visualize/logging_intermediate.rst b/docs/source-pytorch/visualize/logging_intermediate.rst
index f7bfb16565..3c18f80b89 100644
--- a/docs/source-pytorch/visualize/logging_intermediate.rst
+++ b/docs/source-pytorch/visualize/logging_intermediate.rst
@@ -61,7 +61,7 @@ Multiple loggers support visualizing the model topology. Here's an example that
 .. code-block:: python
 
     def any_lightning_module_function_or_hook(self):
-        tensorboard_logger = self.logger.experiment
+        tensorboard_logger = self.logger
 
         prototype_array = torch.Tensor(32, 1, 28, 27)
         tensorboard_logger.log_graph(model=self, input_array=prototype_array)