optional ScaleSpec spec = 2;

  // current status of the scale. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status. Read-only.
  // +optional
  optional ScaleStatus status = 3;
}

// ScaleSpec describes the attributes of a scale subresource
message ScaleSpec {
  // desired number of instances for the scaled object.
  // +optional
  optional int32 replicas = 1;
}

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  // Variables to control TFL Numeric Verify
  NumericVerifySpec numeric_verify_spec;

  // Variables related to quantization
  QuantSpec quant_spec;
};

// Re-calculates scales again in float instead of simply downcasting existing
// scales.
quant::QuantizedType DownCastScale(quant::QuantizedType type,
                                   const SmallVectorImpl<double>& mins,

func (s *Scalar) Add(x, y *Scalar) *Scalar {
	// s = 1 * x + y mod l
	fiatScalarAdd(&s.s, &x.s, &y.s)
	return s
}

// Subtract sets s = x - y mod l, and returns s.
func (s *Scalar) Subtract(x, y *Scalar) *Scalar {
	// s = -1 * y + x mod l
	fiatScalarSub(&s.s, &x.s, &y.s)
	return s
}

// Negate sets s = -x mod l, and returns s.
func (s *Scalar) Negate(x *Scalar) *Scalar {
	// s = -1 * x + 0 mod l

Learn more at: https://kubernetes.io/docs/tasks/debug-application-cluster/logging-stackdriver

## Troubleshooting

In Kubernetes clusters in version 1.10.0 or later, fluentd-gcp DaemonSet can be
manually scaled. This is useful e.g. when applications running in the cluster
are sending a large volume of logs (i.e. over 100kB/s), causing fluentd-gcp to
fail with OutOfMemory errors. Conversely, if the applications aren't generating

			expectedNew: rs("foo-v3", 1, nil, newTimestamp),
			expectedOld: []*apps.ReplicaSet{rs("foo-v2", 5, nil, oldTimestamp), rs("foo-v1", 2, nil, olderTimestamp)},
		},
		// Scales up the new replica set.
		{
			name:          "leftover distribution: 3 -> 4",
			deployment:    newDeployment("foo", 4, nil, nil, nil, nil),
			oldDeployment: newDeployment("foo", 3, nil, nil, nil, nil),

// step. For example:
//
// 2 desired, max unavailable 1%, surge 0% - should scale old(-1), then new(+1), then old(-1), then new(+1)
// 1 desired, max unavailable 1%, surge 0% - should scale old(-1), then new(+1)
// 2 desired, max unavailable 25%, surge 1% - should scale new(+1), then old(-1), then new(+1), then old(-1)
// 1 desired, max unavailable 25%, surge 1% - should scale new(+1), then old(-1)

  // Infer output ranges for activation ops and constants. This is usually
  // required for post-training quantization.
  const bool infer_tensor_range_;

  // Calculate scales in float instead of double, so that the scales and
  // quantized values are exactly the same with the TOCO quantizer.
  const bool legacy_float_scale_;

  // If true, the model is a floating point graph with QDQ ops to be eliminated

    float_min = -float_max;
  }

  ASSERT_THAT(quantized_quant_params.scale, SizeIs(1));
  ASSERT_THAT(quantized_quant_params.zero_point, SizeIs(1));
  float scale = (float_max - float_min) / ((1 << bit_num) - 1);
  EXPECT_THAT(scale, FloatNear(quantized_quant_params.scale[0], eps));
}

class QuantizeModelTest : public testing::Test {
 protected:
  QuantizeModelTest() {

    float_scales.push_back(scales[i]);
    int32_zero_points.push_back(zero_points[i]);
  }
  scale = rewriter.create<TF::ConstOp>(
      loc, scale_type, DenseFPElementsAttr::get(scale_type, float_scales));
  zero_point = rewriter.create<TF::ConstOp>(
      loc, zero_point_type,
      DenseIntElementsAttr::get(zero_point_type, int32_zero_points));
  return success(scale && zero_point);
}