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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 iterpoints(resp: dict, parser: Optional[Callable] = None) -> Iterator[Any]: """Iterates a response JSON yielding data point by point. Can be used with both regular and chunked responses. By default, returns just a plain list of values representing each point, without column names, or other metadata. In case a specific format is needed, an optional ``parser`` argument can be passed. ``parser`` is a function/callable that takes data point values and, optionally, a ``meta`` parameter containing which takes a dictionary containing all or a subset of the following: ``{'columns', 'name', 'tags', 'statement_id'}``. Sample parser functions: .. code:: python # Function optional meta argument def parser(*x, meta):
 return dict(zip(meta['columns'], x)) # Namedtuple (callable) from collections import namedtuple parser = namedtuple('MyPoint', ['col1', 'col2', 'col3']) :param resp: Dictionary containing parsed JSON (output from InfluxDBClient.query) :param parser: Optional parser function/callable :return: Generator object """

    for statement in resp['results']:
        if 'series' not in statement:
            continue
        for series in statement['series']:
            if parser is None:
                return (x for x in series['values'])
            elif 'meta' in inspect.signature(parser).parameters:
                meta = {k: series[k] for k in series if k != 'values'}
                meta['statement_id'] = statement['statement_id']
                return (parser(*x, meta=meta) for x in series['values'])
            else:
                return (parser(*x) for x in series['values'])
    return iter([])