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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 mixture_channel( val: Any, default: Any = RaiseTypeErrorIfNotProvided) -> Sequence[ Tuple[float, np.ndarray]]: """Return a sequence of tuples for a channel that is a mixture of unitaries. In contrast to `mixture` this method falls back to `unitary` if `_mixture_` is not implemented. A mixture channel is described by an iterable of tuples of the form (probability of unitary, unitary) The probability components of the tuples must sum to 1.0 and be between 0 and 1 (inclusive) and the `unitary` must be a unitary matrix. Args: val: The value whose mixture_channel is being computed. default: A default value if val does not support mixture. Returns: An iterable of tuples of size 2. The first element of the tuple is a probability (between 0 and 1) and the second is the unitary that occurs with that probability. The probabilities will sum to 1.0. """

    mixture_getter = getattr(val, '_mixture_', None)
    result = NotImplemented if mixture_getter is None else mixture_getter()
    if result is not NotImplemented:
        return result

    unitary_getter = getattr(val, '_unitary_', None)
    result = NotImplemented if unitary_getter is None else unitary_getter()
    if result is not NotImplemented:
        return ((1.0, result),)

    if default is not RaiseTypeErrorIfNotProvided:
        return default

    if mixture_getter is None and unitary_getter is None:
        raise TypeError(
            "object of type '{}' has no _mixture_ or _unitary_ method."
                .format(type(val)))

    raise TypeError("object of type '{}' does have a _mixture_ or _unitary_ "
                    "method, but it returned NotImplemented.".format(type(val)))