@Override
    double singleQuantile(int index, int scale, double[] dataset) {
      return Quantiles.scale(scale).index(index).computeInPlace(dataset);
    }

    @Override
    Map<Integer, Double> multipleQuantiles(
        Collection<Integer> indexes, int scale, double[] dataset) {
      return Quantiles.scale(scale).indexes(indexes).computeInPlace(dataset);
    }
  },
  ;

  /**

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      return Quantiles.scale(scale).index(index).computeInPlace(dataset);
    }

    @Override
    Map<Integer, Double> multipleQuantiles(
        Collection<Integer> indexes, int scale, double[] dataset) {
      return Quantiles.scale(scale).indexes(indexes).computeInPlace(dataset);
    }
  },
  ;

  /**

MinIO is a cloud-native application designed to scale in a sustainable manner in multi-tenant environments. Orchestration platforms provide perfect launchpad for MinIO to scale. Below is the list of MinIO deployment documents for various orchestration platforms:

MinIO is a high performance distributed object storage server, designed for large-scale private cloud infrastructure. MinIO is designed in a cloud-native manner to scale sustainably in multi-tenant environments. Orchestration platforms like Kubernetes provide perfect cloud-native environment to deploy and scale MinIO.

## MinIO Deployment on Kubernetes

There are multiple options to deploy MinIO on Kubernetes:

1. [Standalone Deployment](#standalone-deployment)
2. [Distributed Deployment](#distributed-deployment)
3. [Cloud Scale Deployment](#cloud-scale-deployment)

## 1. Standalone Deployment

To host multiple tenants on a single machine, run one MinIO Server per tenant with a dedicated HTTPS port, configuration, and data directory.

### Creating the Scale-out Backup Repository

- Under the Backup Infrastructure view, click on Scale-out Repositories and click the Add Scale-out Repository button on the ribbon.

- Follow the on screen wizard

	if ct.LengthValue.Valid {
		return ct.LengthValue.Int64, true
	}
	return ct.SQLColumnType.Length()
}

// DecimalSize returns the scale and precision of a decimal type.
func (ct ColumnType) DecimalSize() (precision int64, scale int64, ok bool) {
	if ct.DecimalSizeValue.Valid {
		return ct.DecimalSizeValue.Int64, ct.ScaleValue.Int64, true
	}
	return ct.SQLColumnType.DecimalSize()
}

  optional k8s.io.apimachinery.pkg.apis.meta.v1.GroupVersionKind kind = 2;

  // Resource is the fully-qualified resource being requested (for example, v1.pods)
  optional k8s.io.apimachinery.pkg.apis.meta.v1.GroupVersionResource resource = 3;

  // SubResource is the subresource being requested, if any (for example, "status" or "scale")
  // +optional
  optional string subResource = 4;

  optional k8s.io.apimachinery.pkg.apis.meta.v1.GroupVersionKind kind = 2;

  // Resource is the fully-qualified resource being requested (for example, v1.pods)
  optional k8s.io.apimachinery.pkg.apis.meta.v1.GroupVersionResource resource = 3;

  // SubResource is the subresource being requested, if any (for example, "status" or "scale")
  // +optional
  optional string subResource = 4;

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems