// AUTO-GENERATED FUNCTIONS START HERE. DO NOT EDIT.
var map_ContainerResourceMetricSource = map[string]string{

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/autoscaling/v2beta2";

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to

    return {0, 0};
  }

  return {static_cast<int32_t>(quantized_multiplier), shift};
}

// Calculates the quantized range for a given scale, zero point, minimum and
// maximum values, and quantization range.
//
// Args:
//   scale: The scale factor for the quantized values.
//   zero_point: The zero point for the quantized values.
//   rmin: The minimum value of the quantized values.

		value  int64
		max    int64
		result int64
		scale  int32
	}{
		{100, 10, 1, 2},
		{100, 10, 1, 2},
		{100, 100, 1, 1},
		{1, 10, 1, 0},
	} {
		r, s := removeInt64Factors(test.value, test.max)
		if r != test.result {
			t.Errorf("%v: unexpected result: %d", test, r)
		}
		if s != test.scale {
			t.Errorf("%v: unexpected scale: %d", test, s)
		}
	}
}

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

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/autoscaling/v2beta1";

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to

  %0 = "tfr.cast"(%arg0) : (tensor<1x3x!quant.calibrated<f32<-1.0:1.0>>>) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%0) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  func.return %scale, %zp: !tfr.tensor, !tfr.tensor

// CHECK: %[[scale:.*]], %[[zp:.*]] = tfr.quant_qparam(%[[input:.*]]) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
// CHECK: return %[[scale]], %[[zp]]
}

// -----

	// scaleTargetRef points to the target resource to scale, and is used to the pods for which metrics
	// should be collected, as well as to actually change the replica count.
	ScaleTargetRef CrossVersionObjectReference `json:"scaleTargetRef" protobuf:"bytes,1,opt,name=scaleTargetRef"`
	// minReplicas is the lower limit for the number of replicas to which the autoscaler
	// can scale down.  It defaults to 1 pod.  minReplicas is allowed to be 0 if the

// CHECK-NEXT:        scale: [ 0.021827 ],
// CHECK-NEXT:        zero_point: [ 151 ]
// CHECK-NEXT:      },
// CHECK-NEXT:      has_rank: true
// CHECK-NEXT:    }, {
// CHECK-NEXT:      shape: [ 32 ],
// CHECK-NEXT:      type: INT32,
// CHECK-NEXT:      buffer: 5,
// CHECK-NEXT:      name: "tfl.pseudo_qconst1",
// CHECK-NEXT:      quantization: {
// CHECK-NEXT:        scale: [ 0.000171 ],

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.