How to use the datafiles.converters.map_type function in datafiles

item_cls = Converter
                                    break
                        else:
                            log.warn(f'{name!r} list type cannot be inferred')
                            item_cls = Converter

                        log.debug(f'Inferring {name!r} type: {cls} of {item_cls}')
                        self.attrs[name] = map_type(cls, name=name, item_cls=item_cls)
                    elif issubclass(cls, dict):
                        cls.__origin__ = dict

                        log.debug(f'Inferring {name!r} type: {cls}')
                        self.attrs[name] = map_type(cls, name=name, item_cls=Converter)
                    else:
                        log.debug(f'Inferring {name!r} type: {cls}')
                        self.attrs[name] = map_type(cls, name=name)

            for name, converter in self.attrs.items():
                log.debug(f"Converting '{name}' data with {converter}")

                if getattr(converter, 'DATACLASS', None):
                    self._set_dataclass_value(data, name, converter)
                else:
                    self._set_attribute_value(data, name, converter, _first)

            hooks.apply(self._instance, self)

        self.modified = False

if issubclass(cls, list):
                        cls.__origin__ = list

                        if value:
                            item_cls = type(value[0])
                            for item in value:
                                if not isinstance(item, item_cls):
                                    log.warn(f'{name!r} list type cannot be inferred')
                                    item_cls = Converter
                                    break
                        else:
                            log.warn(f'{name!r} list type cannot be inferred')
                            item_cls = Converter

                        log.debug(f'Inferring {name!r} type: {cls} of {item_cls}')
                        self.attrs[name] = map_type(cls, name=name, item_cls=item_cls)
                    elif issubclass(cls, dict):
                        cls.__origin__ = dict

                        log.debug(f'Inferring {name!r} type: {cls}')
                        self.attrs[name] = map_type(cls, name=name, item_cls=Converter)
                    else:
                        log.debug(f'Inferring {name!r} type: {cls}')
                        self.attrs[name] = map_type(cls, name=name)

            for name, converter in self.attrs.items():
                log.debug(f"Converting '{name}' data with {converter}")

                if getattr(converter, 'DATACLASS', None):
                    self._set_dataclass_value(data, name, converter)
                else:
                    self._set_attribute_value(data, name, converter, _first)

# TODO: Move this parsing into config.py
    pattern = getattr(m, 'datafile_pattern', None)
    attrs = getattr(m, 'datafile_attrs', None)
    manual = getattr(m, 'datafile_manual', Meta.datafile_manual)
    defaults = getattr(m, 'datafile_defaults', Meta.datafile_defaults)
    auto_load = getattr(m, 'datafile_auto_load', Meta.datafile_auto_load)
    auto_save = getattr(m, 'datafile_auto_save', Meta.datafile_auto_save)
    auto_attr = getattr(m, 'datafile_auto_attr', Meta.datafile_auto_attr)

    if attrs is None and dataclasses.is_dataclass(obj):
        attrs = {}
        log.debug(f'Mapping attributes for {obj.__class__} object')
        for field in dataclasses.fields(obj):
            self_name = f'self.{field.name}'
            if pattern is None or self_name not in pattern:
                attrs[field.name] = map_type(field.type, name=field.name)

    return Mapper(
        obj,
        attrs=attrs,
        pattern=pattern,
        manual=manual,
        defaults=defaults,
        auto_load=auto_load,
        auto_save=auto_save,
        auto_attr=auto_attr,
        root=root,
    )

m = getattr(obj, 'Meta', None)
    pattern = getattr(m, 'datafile_pattern', None)
    attrs = getattr(m, 'datafile_attrs', None)
    manual = getattr(m, 'datafile_manual', ModelMeta.datafile_manual)
    defaults = getattr(m, 'datafile_defaults', ModelMeta.datafile_defaults)
    auto_load = getattr(m, 'datafile_auto_load', ModelMeta.datafile_auto_load)
    auto_save = getattr(m, 'datafile_auto_save', ModelMeta.datafile_auto_save)
    auto_attr = getattr(m, 'datafile_auto_attr', ModelMeta.datafile_auto_attr)

    if attrs is None and dataclasses.is_dataclass(obj):
        attrs = {}
        log.debug(f'Mapping attributes for {obj.__class__} object')
        for field in dataclasses.fields(obj):
            self_name = f'self.{field.name}'
            if pattern is None or self_name not in pattern:
                attrs[field.name] = map_type(field.type, name=field.name)

    return Mapper(
        obj,
        attrs=attrs,
        pattern=pattern,
        manual=manual,
        defaults=defaults,
        auto_load=auto_load,
        auto_save=auto_save,
        auto_attr=auto_attr,
        root=root,
    )