Update partitioned dataset lazy saving docs

RELEASE.md

@@ -14,6 +14,7 @@
 * Safeguard hooks when user incorrectly registers a hook class in settings.py.
 * Fixed parsing paths with query and fragment.
 * Remove lowercase transformation in regex validation.
+* Updated `Partitioned dataset lazy saving` docs page.
 
 ## Breaking changes to the API
 ## Documentation changes

docs/source/data/partitioned_and_incremental_datasets.md

@@ -175,6 +175,7 @@
   path: s3://my-bucket-name
   dataset: pandas.CSVDataset
   filename_suffix: ".csv"
+  save_lazily: True
 ```
 
 Here is the node definition:
@@ -238,6 +239,24 @@
 When using lazy saving, the dataset will be written _after_ the `after_node_run` [hook](../hooks/introduction).
 ```
 
+```{note}
+Lazy saving is a default behaviour, meaning that if `Callable` type provided the dataset will be written _after_ the `after_node_run` hook.
+```
+
+In some cases, it might be useful to disable such a behaviour, for example, when your object is already `Callable`, like a Tensorflow model, and you do not mean to save it lazily.
+To disable lazy saving set `save_lazily` parameter to `False`:
+
+```yaml
+# conf/base/catalog.yml
+
+new_partitioned_dataset:
+  type: partitions.PartitionedDataset
+  path: s3://my-bucket-name
+  dataset: pandas.CSVDataset
+  filename_suffix: ".csv"
+  save_lazily: False
+```
+
 ## Incremental datasets
 
 {class}`IncrementalDataset<kedro-datasets:kedro_datasets.partitions.IncrementalDataset>` is a subclass of `PartitionedDataset`, which stores the information about the last processed partition in the so-called `checkpoint`. `IncrementalDataset` addresses the use case when partitions have to be processed incrementally, that is, each subsequent pipeline run should process just the partitions which were not processed by the previous runs.