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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def decode(self, initial_state: State, transition_function: TransitionFunction, supervision: SupervisionType) -> Dict[str, torch.Tensor]: """ Takes an initial state object, a means of transitioning from state to state, and a supervision signal, and uses the supervision to train the transition function to pick "good" states. This function should typically return a ``loss`` key during training, which the ``Model`` will use as its loss. Parameters initial_state : ``State`` This is the initial state for decoding, typically initialized after running some kind of encoder on some inputs. transition_function : ``TransitionFunction`` This is the transition function that scores all possible actions that can be taken in a given state, and returns a ranked list of next states at each step of decoding. supervision : ``SupervisionType`` This is the supervision that is used to train the ``transition_function`` function to pick "good" states. You can use whatever kind of supervision you want (e.g., a single "gold" action sequence, a set of possible "gold" action sequences, a reward function, etc.). We use ``typing.Generics`` to make sure that our static type checker is happy with how you've matched the supervision that you provide in the model to the ``DecoderTrainer`` that you want to use. """

        raise NotImplementedError