5. The internal Vowpal Wabbit model is updated with the `BasedOn` input, the chosen `ToSelectFrom` action, and the resulting score from the scorer.
        6. The final response is returned.
    Expected input dictionary format:
        - At least one variable encapsulated within `BasedOn` to serve as the selection criteria.
        - A single list variable within `ToSelectFrom`, representing potential actions for the VW model. This list can take the form of:
            - A list of strings, e.g., `action = ToSelectFrom(["action1", "action2", "action3"])`
            - A list of list of strings e.g. `action = ToSelectFrom([["action1", "another identifier of action1"], ["action2", "another identifier of action2"]])`
            - A list of dictionaries, where each dictionary represents an action with namespace names as keys and corresponding action strings as values. For instance, `action = ToSelectFrom([{"namespace1": ["action1", "another identifier of action1"], "namespace2": "action2"}, {"namespace1": "action3", "namespace2": "action4"}])`.
    Extends:
        RLChain
    Attributes:
        feature_embedder (PickBestFeatureEmbedder, optional): Is an advanced attribute. Responsible for embedding the `BasedOn` and `ToSelectFrom` inputs. If omitted, a default embedder is utilized.
    """  # noqa E501
    def __init__(
        self,
        *args: Any,
        **kwargs: Any,
    ):
        auto_embed = kwargs.get("auto_embed", False)
        feature_embedder = kwargs.get("feature_embedder", None)
        if feature_embedder:
            if "auto_embed" in kwargs: