Update pipeline_registry.md

docs/source/deployment/aws_step_functions.md

@@ -200,7 +200,7 @@ ENTRYPOINT [ "/usr/local/bin/python", "-m", "awslambdaric" ]
 CMD [ "lambda_handler.handler" ]
 ```
 
-This `Dockerfile` is adapted from the official guide on [how to create a custom image](https://docs.aws.amazon.com/lambda/latest/dg/images-create.html#images-create-from-alt) for Lambda to include Kedro-specific steps.
+This `Dockerfile` is adapted from the official guide on [how to create a custom image](https://docs.aws.amazon.com/lambda/latest/dg/images-create.html#images-types) for Lambda to include Kedro-specific steps.
 
 * **Step 2.4**: Build the Docker image and push it to AWS Elastic Container Registry (ECR):
 

docs/source/nodes_and_pipelines/pipeline_registry.md

@@ -3,6 +3,10 @@
 Projects generated using Kedro 0.17.2 or later define their pipelines in `src/<package_name>/pipeline_registry.py`. This, in turn, populates the `pipelines` variable in [`kedro.framework.project`](/kedro.framework.project) that the Kedro CLI and plugins use to access project pipelines. The `pipeline_registry` module must contain a top-level `register_pipelines()` function that returns a mapping from pipeline names to [`Pipeline`](/kedro.pipeline.Pipeline) objects. For example, the [pipeline registry in the Kedro starter for the completed spaceflights tutorial](https://github.com/kedro-org/kedro-starters/blob/0.18.2/spaceflights/%7B%7B%20cookiecutter.repo_name%20%7D%7D/src/%7B%7B%20cookiecutter.python_package%20%7D%7D/pipeline_registry.py) could define the following `register_pipelines()` function that exposes the data processing pipeline, the data science pipeline, and a third, default pipeline that combines both of the aforementioned pipelines:
 
 ```python
+import spaceflights.pipelines.data_processing as dp
+import spaceflights.pipelines.data_science as ds
+
+
 def register_pipelines() -> Dict[str, Pipeline]:
     """Register the project's pipelines.