/* Want to test simple MatMul example:
    [[0,0],    *   [[0,0],    =   [[0,0],
     [0,0]]         [0,0]]         [0,0]]
  */

  // Build an abstract input tensor.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals[] = {0.0f, 0.0f, 0.0f, 0.0f};
  TFE_Context* eager_ctx = TF_ExecutionContextGetTFEContext(ctx, status.get());
  TFE_TensorHandle* t =

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos al mismo tiempo.

  TFE_DeleteContextOptions(opts);

  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Use large matrices so that RPCs don't return before we get a chance
  // to call TFE_DeleteContext.
  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle100x100(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle100x100(ctx);

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

### Major Features and Improvements

*   Added F-Score metrics `tf.keras.metrics.FBetaScore`, `tf.keras.metrics.F1Score`, and `tf.keras.metrics.R2Score`.
*   Added activation function `tf.keras.activations.mish`.

`,
		Example: `  # Retrieve workload metrics for productpage-v1 workload
  istioctl experimental metrics productpage-v1

  # Retrieve workload metrics for various services with custom duration
  istioctl experimental metrics productpage-v1 -d 2m

  # Retrieve workload metrics for various services in the different namespaces
  istioctl experimental metrics productpage-v1.foo reviews-v1.bar ratings-v1.baz`,

type minioCollector struct {
	desc *prometheus.Desc
}

// Describe sends the super-set of all possible descriptors of metrics
func (c *minioCollector) Describe(ch chan<- *prometheus.Desc) {
	ch <- c.desc
}

// Collect is called by the Prometheus registry when collecting metrics.
func (c *minioCollector) Collect(ch chan<- prometheus.Metric) {
	// Expose MinIO's version information

)

const (
	prometheusMetricsPathLegacy     = "/prometheus/metrics"
	prometheusMetricsV2ClusterPath  = "/v2/metrics/cluster"
	prometheusMetricsV2BucketPath   = "/v2/metrics/bucket"
	prometheusMetricsV2NodePath     = "/v2/metrics/node"
	prometheusMetricsV2ResourcePath = "/v2/metrics/resource"

	// Metrics v3 endpoints
	metricsV3Path = "/metrics/v3"
)

// Standard env prometheus auth type
const (

			aggr.Merge(&disk)
		}
		m.Aggregated.Disk = &aggr
	}

	if types.Contains(madmin.MetricsScanner) {
		metrics := globalScannerMetrics.report()
		m.Aggregated.Scanner = &metrics
	}
	if types.Contains(madmin.MetricsOS) {
		metrics := globalOSMetrics.report()
		m.Aggregated.OS = &metrics
	}
	if types.Contains(madmin.MetricsBatchJobs) {
		m.Aggregated.BatchJobs = globalBatchJobsMetrics.report(opts.jobID)
	}

	name := rm.Name
	metrics := make([]MetricV2, 0, 3)
	metrics = append(metrics, MetricV2{
		Description:    getResourceMetricDescription(subSys, name, help),
		Value:          rm.Current,
		VariableLabels: cloneMSS(rm.Labels),
	})

	if requireAvgMax {
		avgName := MetricName(fmt.Sprintf("%s_avg", name))
		avgHelp := fmt.Sprintf("%s (avg)", help)
		metrics = append(metrics, MetricV2{