How to use the kedro.pipeline.node function in kedro

def str_node_inputs_list():
    return {
        "nodes": [
            node(biconcat, ["input1", "input2"], ["input3"], name="node1"),
            node(identity, "input3", "input4", name="node2"),
        ],
        "expected": [
            {node(biconcat, ["input1", "input2"], ["input3"], name="node1")},
            {node(identity, "input3", "input4", name="node2")},
        ],
        "free_inputs": ["input1", "input2"],
        "outputs": ["input4"],
    }

def branchless_no_input_pipeline():
    """The pipeline runs in the order A->B->C->D->E."""
    return Pipeline(
        [
            node(identity, "D", "E", name="node1"),
            node(identity, "C", "D", name="node2"),
            node(identity, "A", "B", name="node3"),
            node(identity, "B", "C", name="node4"),
            node(random, None, "A", name="node5"),
        ]

def test_tag_nodes(self):
        tagged_node = node(identity, "input", "output", tags=["hello"]).tag(["world"])
        assert "hello" in tagged_node.tags
        assert "world" in tagged_node.tags
        assert len(tagged_node.tags) == 2

def test_lambda(self):
        n = node(lambda a: a, ["in"], ["out"])
        assert str(n) == "([in]) -> [out]"
        assert n.name == "([in]) -> [out]"
        assert n.short_name == ""

def disjoint_pipeline():
    # Two separate pipelines: A->B->C and D->E->F
    return {
        "nodes": [
            node(identity, "A", "B", name="node1"),
            node(identity, "B", "C", name="node2"),
            node(identity, "E", "F", name="node3"),  # disjoint part D->E->F
            node(identity, "D", "E", name="node4"),
        ],
        "expected": [
            {
                node(identity, "A", "B", name="node1"),
                node(identity, "D", "E", name="node4"),
            },
            {
                node(identity, "B", "C", name="node2"),
                node(identity, "E", "F", name="node3"),
            },
        ],
        "free_inputs": ["A", "D"],
        "outputs": ["C", "F"],
    }

def test_node_equals(self):
        first = node(identity, "input1", "output1", name="a node")
        second = node(identity, "input1", "output1", name="a node")
        assert first == second
        assert first is not second

def nodes_with_tags():
    return [
        node(identity, "E", None, name="node1"),
        node(identity, "D", "E", name="node2", tags=["tag1", "tag2"]),
        node(identity, "C", "D", name="node3"),
        node(identity, "A", "B", name="node4", tags=["tag2"]),
        node(identity, "B", "C", name="node5"),
        node(constant_output, None, "A", name="node6", tags=["tag1"]),
    ]

[
                    node(
                        treatment_fractions_,
                        ["args_raw", "df_00"],
                        "treatment_fractions",
                    )
                ],
                tags="131_treatment_fractions_",
            ),
            Pipeline(
                [node(fit_propensity, ["args", "df_00"], "propensity_model")],
                tags="211_fit_propensity",
            ),
            Pipeline(
                [
                    node(
                        estimate_propensity,
                        ["args", "df_00", "propensity_model"],
                        "df_01",
                    )
                ],
                tags="221_estimate_propensity",
            ),
            Pipeline(
                [
                    node(
                        model_for_treated_fit, ["args", "df_01"], "treated__model_dict"
                    ),
                    node(
                        model_for_untreated_fit,
                        ["args", "df_01"],
                        "untreated__model_dict",

Returns:
        Pipeline: The resulting pipeline.

    """

    ###########################################################################
    # Here you can find an example pipeline with 4 nodes.
    #
    # PLEASE DELETE THIS PIPELINE ONCE YOU START WORKING ON YOUR OWN PROJECT AS
    # WELL AS THE FILE nodes/example.py
    # -------------------------------------------------------------------------

    pipeline = Pipeline(
        [
            node(
                split_data,
                ["example_iris_data", "parameters"],
                dict(
                    train_x="example_train_x",
                    train_y="example_train_y",
                    test_x="example_test_x",
                    test_y="example_test_y",
                ),
            ),
            node(
                train_model,
                ["example_train_x", "example_train_y", "parameters"],
                "example_model",
            ),
            node(
                predict,

],
                tags="321_predict_proba",
            ),
            Pipeline(
                [
                    node(
                        compute_cate,
                        ["treated__proba", "untreated__proba"],
                        "cate_estimated",
                    )
                ],
                tags="411_compute_cate",
            ),
            Pipeline(
                [
                    node(
                        add_cate_to_df,
                        [
                            "args",
                            "df_01",
                            "cate_estimated",
                            "treated__proba",
                            "untreated__proba",
                        ],
                        "df_02",
                    )
                ],
                tags="421_add_cate_to_df",
            ),
            Pipeline(
                [
                    node(