// CHECK-LABEL: @unfuse_batch_norm
// CHECK-SAME: %[[X:[^:[:space:]]+]]
// CHECK-SAME: %[[SCALE:[^:[:space:]]+]]
// CHECK-SAME: %[[OFFSET:[^:[:space:]]+]]
// CHECK-SAME: %[[MEAN:[^:[:space:]]+]]
// CHECK-SAME: %[[VARIANCE:[^:[:space:]]+]]
func.func @unfuse_batch_norm(
    %x: tensor<4x256xf32>, %scale: tensor<256xf32>, %offset: tensor<256xf32>,
    %mean: tensor<256xf32>, %variance: tensor<256xf32>)

            "type": "typeValue",
            "value": "0",
            "averageValue": "0",
            "averageUtilization": 4
          }
        }
      }
    ],
    "behavior": {
      "scaleUp": {
        "stabilizationWindowSeconds": 3,
        "selectPolicy": "selectPolicyValue",
        "policies": [
          {
            "type": "typeValue",
            "value": 2,

	// overflow.
	//
	// An alpha-premultiplied color component c has been scaled by alpha (a),
	// so has valid values 0 <= c <= a.
	RGBA() (r, g, b, a uint32)
}

// RGBA represents a traditional 32-bit alpha-premultiplied color, having 8
// bits for each of red, green, blue and alpha.
//
// An alpha-premultiplied color component C has been scaled by alpha (A), so
// has valid values 0 <= C <= A.
type RGBA struct {

tfr.func @tf__my_requantize(%input: !tfr.tensor) -> !tfr.tensor {
  %raw_data = tfr.quant_raw_data(%input) : (!tfr.tensor) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%input) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  %result = tfr.call @tf__requantize(%raw_data, %scale, %zp) : (!tfr.tensor, !tfr.tensor, !tfr.tensor) -> !tfr.tensor
  tfr.return %result : !tfr.tensor
}

// CHECK-LABEL: @tf__intermediate

          - --container=metrics-server
          - --poll-period=30000
          - --estimator=exponential
          # Specifies the smallest cluster (defined in number of nodes)
          # resources will be scaled to.
          - --minClusterSize={{ metrics_server_min_cluster_size }}
          # Use kube-apiserver metrics to avoid periodically listing nodes.
          - --use-metrics=true
      volumes:

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.

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:

  // quantization dim is 3, so we can cache all the converters.
  ShapedType type = attr.getType();
  std::size_t dim_size = type.getDimSize(quantization_dim_);
  if (dim_size != scales_.size()) {
    return {};
  }
  SmallVector<UniformQuantizedValueConverter, 4> converters;
  converters.reserve(dim_size);
  for (int i = 0, e = dim_size; i != e; ++i) {
    converters.push_back(getPerChunkConverter(i));

// in the range [a, b].
//
// This distribution is *not* normalized (it is not a probability
// distribution). This makes it easier to work with as it's being
// updated.
//
// It is represented as the sum of scaled uniform distribution
// functions and Dirac delta functions (which are treated as
// degenerate uniform distributions).
type mud struct {
	sorted, unsorted []edge

	EXTRACTPS $4, X2, (BX)          // ERROR "invalid instruction"
	EXTRACTPS $-1, X2, (BX)         // ERROR "invalid instruction"
	// VSIB addressing does not permit non-vector (X/Y)
	// scaled index register.
	VPGATHERDQ X12,(R13)(AX*2), X11 // ERROR "invalid instruction"
	VPGATHERDQ X2, 664(BX*1), X1    // ERROR "invalid instruction"
	VPGATHERDQ Y2, (BP)(AX*2), Y1   // ERROR "invalid instruction"