def METHOD_NAME(self) -> List[Instance]:
    """Returns the instances for this scenario."""
    def format_str(unformatted_str: str) -> str:
        formatted_str = unformatted_str.strip()
        if formatted_str[-1] != ".":
            formatted_str = formatted_str + "."
        return formatted_str
    def split_multiple_answer_string(multiple_answers: str, seperator=";") -> List[str]:
        return [format_str(a.strip()) for a in multiple_answers.split(seperator) if a.strip()]
    def get_references(best_answer: str, incorrect_answers: List[str]) -> List[Reference]:
        # Prepare the references list
        references = [Reference(Output(text=ans), tags=[]) for ans in incorrect_answers]
        references.append(Reference(Output(text=best_answer), tags=[CORRECT_TAG]))
        # To ensure that we have some variety at where the option with the correct answer
        # appears (A, B, C etc.) we use ascii value of the first character of the best_answer
        # string (ord) and use ord mod the list length to rotate the references list.
        k = ord(best_answer[0]) % len(references)
        references = references[k:] + references[:k]
        return references
    def get_split_instances(split: str, data: List[Dict[str, Any]]) -> List[Instance]:
        instances: List[Instance] = []
        for dt in data:
            if self.task == self.MULTIPLE_CHOICE_SINGLE_ANSWER:
                # Format the fields of the question
                question: str = dt["question"].strip()
                best_answer: str = format_str(dt["best_answer"])
                incorrect_answers: List[str] = split_multiple_answer_string(dt["incorrect_answers"])
                # Prepare the instance
                references = get_references(best_answer, incorrect_answers)
                instance = Instance(
                    input=Input(text=question),
                    references=references,
                    split=split,
                )
                instances.append(instance)
        return instances
    # Body of the function
    self.download_dataset()
    data = self.load_dataset()
    split_k = int(len(data) * self.TRAIN_RATIO)
    train_instances: List[Instance] = get_split_instances(TRAIN_SPLIT, data[:split_k])
    valid_instances: List[Instance] = get_split_instances(VALID_SPLIT, data[split_k:])
    return train_instances + valid_instances