Fix inconsistency in API docs Pipeline & Node (#5256)

Signed-off-by: Merel Theisen <merel.theisen@quantumblack.com>

Author: merelcht

docs/api/pipeline/kedro.pipeline.Pipeline.md

@@ -1,4 +1,3 @@
-::: kedro.pipeline.Pipeline
+::: kedro.pipeline.pipeline
     options:
-      members: true
       show_source: true

docs/api/pipeline/kedro.pipeline.md

@@ -8,5 +8,5 @@
 
 | Name                          | Type       | Description                                      |
 |-------------------------------|------------|--------------------------------------------------|
-| [`kedro.pipeline.node`](kedro.pipeline.node.md) | Function   | A decorator to define a node in a Kedro pipeline. |
+| [`kedro.pipeline.Node`](kedro.pipeline.node.md) | Class   | A class to define a node in a Kedro pipeline. |
 | [`kedro.pipeline.Pipeline`](kedro.pipeline.Pipeline.md)| Class      | Represents a Kedro pipeline.                    |

docs/build/pipeline_introduction.md

@@ -2,7 +2,7 @@
 
 We previously introduced [Nodes](./nodes.md) as building blocks that represent tasks, and can be combined in a pipeline to build your workflow. A pipeline organises the dependencies and execution order of your collection of nodes, and connects inputs and outputs while keeping your code modular. The pipeline resolves dependencies to determine the node execution order, and does *not* necessarily run the nodes in the order in which they are passed in.
 
-To benefit from Kedro's automatic dependency resolution, you can chain your nodes into a [kedro.pipeline.Pipeline][] object, which is a list of nodes that use a shared set of variables. That class can be instantiated using the [kedro.pipeline.Pipeline][] constructor, based on nodes or other pipelines (in which case all nodes from that pipeline will be used).
+To benefit from Kedro's automatic dependency resolution, you can chain your nodes into a [`Pipeline`][kedro.pipeline.pipeline.Pipeline] object, which is a list of nodes that use a shared set of variables. That class can be instantiated using the [`Pipeline`][kedro.pipeline.pipeline.Pipeline] constructor, based on nodes or other pipelines (in which case all nodes from that pipeline will be used).
 
 The following sections explain how to create and use Kedro pipelines:
 

docs/build/pipeline_registry.md

@@ -1,6 +1,6 @@
 # The pipeline registry
 
-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/main/spaceflights-pandas/{{ cookiecutter.repo_name }}/src/{{ cookiecutter.python_package }}/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:
+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.Pipeline] objects. For example, the [pipeline registry in the Kedro starter for the completed spaceflights tutorial](https://github.com/kedro-org/kedro-starters/blob/main/spaceflights-pandas/{{ cookiecutter.repo_name }}/src/{{ cookiecutter.python_package }}/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
@@ -44,11 +44,11 @@ def register_pipelines() -> Dict[str, Pipeline]:
     return pipelines
 ```
 
-Under the hood, the `find_pipelines()` function traverses the `src/<package_name>/pipelines/` directory and returns a mapping from pipeline directory name to [`Pipeline`][kedro.pipeline.Pipeline] object by:
+Under the hood, the `find_pipelines()` function traverses the `src/<package_name>/pipelines/` directory and returns a mapping from pipeline directory name to [`Pipeline`][kedro.pipeline.pipeline.Pipeline] object by:
 
 1. Importing the `<package_name>.pipelines.<pipeline_name>` module
 2. Calling the `create_pipeline()` function exposed by the `<package_name>.pipelines.<pipeline_name>` module
-3. Validating that the constructed object is a [`Pipeline`][kedro.pipeline.Pipeline]
+3. Validating that the constructed object is a [`Pipeline`][kedro.pipeline.pipeline.Pipeline]
 
 By default, if any of these steps fail, `find_pipelines()` (or `find_pipelines(raise_errors=False)`) raises an appropriate warning and skips the current pipeline but continues traversal. During development, this enables you to run your project with some pipelines, even if other pipelines are broken or works in progress.
 

docs/extend/common_use_cases.md

@@ -4,7 +4,7 @@ Kedro has a few built-in mechanisms for you to extend its behaviour. This docume
 
 ## Use Case 1: How to add extra behaviour to Kedro's execution timeline
 
-The execution timeline of a Kedro pipeline can be thought of as a sequence of actions performed by various Kedro library components, such as the [kedro-datasets documentation](https://docs.kedro.org/projects/kedro-datasets/en/stable/), [kedro.io.DataCatalog][], [kedro.pipeline.Pipeline][], [kedro.pipeline.node.Node][] and [kedro.framework.context.KedroContext][].
+The execution timeline of a Kedro pipeline can be thought of as a sequence of actions performed by various Kedro library components, such as the [kedro-datasets documentation](https://docs.kedro.org/projects/kedro-datasets/en/stable/), [kedro.io.DataCatalog][], [kedro.pipeline.pipeline.Pipeline][], [kedro.pipeline.node.Node][] and [kedro.framework.context.KedroContext][].
 
 At different points in the lifecycle of these components, you might want to add extra behaviour: for example, you could add extra computation for profiling purposes _before_ and _after_ a node runs, or _before_ and _after_ the I/O actions of a dataset, namely the `load` and `save` actions.
 

mkdocs.yml

@@ -430,7 +430,7 @@ nav:
               - kedro.logging: api/kedro.logging.md
               - kedro.pipeline:
                   - Overview: api/pipeline/kedro.pipeline.md
-                  - node: api/pipeline/kedro.pipeline.node.md
+                  - Node: api/pipeline/kedro.pipeline.node.md
                   - Pipeline: api/pipeline/kedro.pipeline.Pipeline.md
               - kedro.runner:
                   - Overview: api/runner/kedro.runner.md