// (indicating that the batch contains only CPU samples) and adds each
// sample contained therein to the provided samples list.
func addCPUSamples(samples []cpuSample, b batch) ([]cpuSample, error) {
	if !b.isCPUSamplesBatch() {
		return nil, fmt.Errorf("internal error: addCPUSamples called on non-CPU-sample batch")
	}
	r := bytes.NewReader(b.data)

	}

	// provider.sampler > telemetry.RandomSamplingPercentage > defaultConfig.tracing.sampling > PILOT_TRACE_SAMPLING
	var sampling float64
	if useCustomSampler {
		// If the TracingProvider has a custom sampler (OTel Sampler)
		// the sampling percentage is set to 100% so all spans arrive at the sampler for its decision.
		sampling = 100
	} else if spec.RandomSamplingPercentage != nil {

	descs := metrics.All()
	var samples [2][]metrics.Sample
	samples[0] = make([]metrics.Sample, len(descs))
	samples[1] = make([]metrics.Sample, len(descs))
	total := 0
	for i := range samples[0] {
		if !descs[i].Cumulative {
			continue
		}
		samples[0][total].Name = descs[i].Name
		total++
	}
	samples[0] = samples[0][:total]
	samples[1] = samples[1][:total]
	copy(samples[1], samples[0])

            excludeTestsMatching "org.gradle.docs.samples.*.building-swift*.sample"
            // We don't have Android SDK installed on mac M1 now
            excludeTestsMatching "org.gradle.docs.samples.*.building-android-*.sample"
            excludeTestsMatching "org.gradle.docs.samples.*.structuring-software-projects*android-app.sample"
        }

								Value: "custom-value",
							},
							AppendAction: core.HeaderValueOption_OVERWRITE_IF_EXISTS_OR_ADD,
						},
					},
				},
				Sampler: &core.TypedExtensionConfig{
					Name: "envoy.tracers.opentelemetry.samplers.dynatrace",
					TypedConfig: protoconv.MessageToAny(&otelsamplers.DynatraceSamplerConfig{
						Tenant:             tc.dtTenant,
						ClusterId:          tc.dtClusterID,

apiVersion: release-notes/v2
kind: feature
area: telemetry
issue:
- 50001
releaseNotes:
  - |

        sample_proto = _RepresentativeDataSample.FromString(
            sample_bytes.numpy()
        )
        sample = {}
        for input_key, tensor_proto in sample_proto.tensor_proto_inputs.items():
          sample[input_key] = tensor_util.MakeNdarray(tensor_proto)
        samples.append(sample)
    return samples

  def load(self) -> RepresentativeDatasetMapping:
    """Loads the representative datasets.

    Returns:

			}
			// All samples should point to one location.
			var gotSamples []*profile.Sample
			for _, sample := range p.Sample {
				var locs []*profile.Location
				for _, loc := range sample.Location {
					locs = append(locs, &profile.Location{ID: loc.ID})
				}
				gotSamples = append(gotSamples, &profile.Sample{Value: sample.Value, Location: locs})
			}

}

// scaleHeapSample adjusts the data from a heap Sample to
// account for its probability of appearing in the collected
// data. heap profiles are a sampling of the memory allocations
// requests in a program. We estimate the unsampled value by dividing
// each collected sample by its probability of appearing in the
// profile. heap profiles rely on a poisson process to determine
// which samples to collect, based on the desired average collection

	const expectedLocationNewInliner = "runtime/pprof.TestGenericsInlineLocations;" + expectedLocation
	var s *profile.Sample
	for _, sample := range p.Sample {
		if sampleToString(sample) == expectedSample {
			s = sample
			break
		}
	}
	if s == nil {
		t.Fatalf("expected \n%s\ngot\n%s", expectedSample, strings.Join(profileToStrings(p), "\n"))
	}
	loc := s.Location[0]