for input_key in sample_1:
        self._assert_tensorlike_all_close(
            sess, sample_1[input_key], sample_2[input_key]
        )

  def test_not_implemented_saver(self):
    with self.assertRaisesRegex(
        NotImplementedError, '"save" is not implemented.'
    ):
      repr_dataset.RepresentativeDatasetSaver().save(representative_dataset={})

        outputContains("org.gradle:sample:1.0 (default capability)")
    }

    def "attribute combinations can be repeated if capabilities differ without a warning"() {
        given:
        settingsFile << "rootProject.name = 'sample'"
        buildFile << """
            plugins {

  TF_DeleteBuffer(result1);
  TFE_MonitoringDeleteSampler1(sampler1);

  auto* sampler2 = TFE_MonitoringNewSampler2("test/sampler2", buckets, status,
                                             "test", "label1", "label2");
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  auto* cell2 = TFE_MonitoringGetCellSampler2(sampler2, "foo", "bar");
  TFE_MonitoringSamplerCellAdd(cell2, 2.0);

// (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,

====
include::sample[dir="samples/build-organization/composite-builds/basic/kotlin",files="build.gradle.kts[tags=run]"]
include::sample[dir="samples/build-organization/composite-builds/basic/groovy",files="build.gradle[tags=run]"]
====

Or you can declare a dependency on tasks with a certain path in some or all of the included builds:

====

        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: