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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 _normalize(self, name, columns, points):
 """Normalize data for the InfluxDB's data model."""


        for i, _ in enumerate(points):
            # Supported type:
            # https://docs.influxdata.com/influxdb/v1.5/write_protocols/line_protocol_reference/
            if points[i] is None:
                # Ignore points with None value
                del(points[i])
                del(columns[i])
                continue
            try:
                points[i] = float(points[i])
            except (TypeError, ValueError):
                pass
            else:
                continue
            try:
                points[i] = str(points[i])
            except (TypeError, ValueError):
                pass
            else:
                continue

        return [{'measurement': name,
                 'tags': self.parse_tags(self.tags),
                 'fields': dict(zip(columns, points))}]