def _convert(metric, scraper_config=None):
    for (index, sample) in enumerate(metric.samples):
        val = sample[self.SAMPLE_VALUE]
        if (not self._is_value_valid(val)):
            self.log.debug('Metric value is not supported for metric %s', sample[self.SAMPLE_NAME])
            continue
        if sample[self.SAMPLE_NAME].endswith('_sum'):
            lst = list(sample)
            lst[self.SAMPLE_VALUE] = converter(val)
            metric.samples[index] = tuple(lst)
        elif (sample[self.SAMPLE_NAME].endswith('_bucket') and ('Inf' not in sample[self.SAMPLE_LABELS]['le'])):
            sample[self.SAMPLE_LABELS]['le'] = str(converter(float(sample[self.SAMPLE_LABELS]['le'])))
    self.submit_openmetric(metric_name, metric, scraper_config)
def _convert(metric, scraper_config=None):
    for (index, sample) in enumerate(metric.samples):
        val = sample[self.SAMPLE_VALUE]
        if (not self._is_value_valid(val)):
            self.log.debug('Metric value is not supported for metric %s', sample[self.SAMPLE_NAME])
            continue
        if sample[self.SAMPLE_NAME].endswith('_sum'):
            lst = list(sample)
            lst[self.SAMPLE_VALUE] = converter(val)
            metric.samples[index] = tuple(lst)
        elif (sample[self.SAMPLE_NAME].endswith('_bucket') and ('Inf' not in sample[self.SAMPLE_LABELS]['le'])):
            sample[self.SAMPLE_LABELS]['le'] = str(converter(float(sample[self.SAMPLE_LABELS]['le'])))
    self.submit_openmetric(metric_name, metric, scraper_config)