f32], _support_output_type_float_in_quantized_op = true, detections_per_class = 100 : i64, device = "", h_scale = 5.000000e+00 : f32, max_classes_per_detection = 1 : i64, max_detections = 20 : i64, nms_iou_threshold = 6.000000e-01 : f32, nms_score_threshold = 3.000000e-01 : f32, num_classes = 90 : i64, use_regular_nms = false, w_scale = 5.000000e+00 : f32, x_scale = 1.000000e+01 : f32, y_scale = 1.000000e+01 : f32} : (tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>) -> (te...

        auto shape = GetAsVector(quant_tensor->shape());
        if (kUseUpdatedHybridSchemeDefault) {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), shape[0]);
        } else {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), 1);
        }
      } else {
        EXPECT_EQ(quant_tensor->type(), TensorType_FLOAT32);
      }
    }

colors are first mapped into HSV. A scale is then applied all the saturation
values, and then remapped back to RGB colorspace.
  }];

  let arguments = (ins
    Arg<TensorOf<[TF_Float16, TF_Float32]>, [{Images to adjust.  At least 3-D.}]>:$images,
    Arg<TF_Float32Tensor, [{A float scale to add to the saturation.}]>:$scale
  );

  let results = (outs

	// owner: @arjunrn @mwielgus @josephburnett @sanposhiho
	// kep: https://kep.k8s.io/1610
	// alpha: v1.20
	// beta:  v1.27
	// GA:  v1.30
	//
	// Add support for the HPA to scale based on metrics from individual containers
	// in target pods
	HPAContainerMetrics featuregate.Feature = "HPAContainerMetrics"

	// owner: @dxist
	// alpha: v1.16
	//

- Kube-controller-manager: the `horizontal-pod-autoscaler-upscale-delay` and `horizontal-pod-autoscaler-downscale-delay` flags have been removed (deprecated and non-functional since v1.12) ([#124948](https://github.com/kubernetes/kubernetes/pull/124948), [@SataQiu](https://github.com/SataQiu)) [SIG API Machinery,...

                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value grad = op.getYBackprop();
    Value act = op.getX();
    Value scale = op.getScale();
    Value mean = op.getReserveSpace_1();
    Value var = op.getReserveSpace_2();

    // TODO(b/141785544): Update this to not require static shapes.

					}

					// MaxExtenderPriority may diverge from the max priority used in the scheduler and defined by MaxNodeScore,
					// therefore we need to scale the score returned by extenders to the score range used by the scheduler.
					finalscore := score * weight * (framework.MaxNodeScore / extenderv1.MaxExtenderPriority)

					if allNodeExtendersScores[nodename] == nil {

			for i := range o.Registers {
				Arch.LoadRegResult(&s, f, rts[i], ssa.ObjRegForAbiReg(o.Registers[i], f.Config), n, offs[i])
			}
		}

		s.pp.Prog(obj.ARET)
	}

	if inlMarks != nil {
		hasCall := false

		// We have some inline marks. Try to find other instructions we're
		// going to emit anyway, and use those instructions instead of the
		// inline marks.
		for p := s.pp.Text; p != nil; p = p.Link {

`Policy` optionally took in a `tf.compat.v1.mixed_precision.LossScale` in the constructor, which defaulted to a dynamic loss scale for the `"mixed_float16"` policy and no loss scale for other policies. In `Model.compile`, if the model's policy had a loss scale, the optimizer would be wrapped with a `LossScaleOptimizer`. With the non-experimental `Policy`, there is no loss scale associated with the `Policy`, and `Model.compile` wraps the optimizer with a `LossScaleOptimizer` if and only if the policy...

- From now on, the HPA controller will return an error for the container resource metrics when the feature gate "HPAContainerMetrics" is disabled. As a result, HPA with a container resource metric performs no scale-down and performs only scale-up based on other metrics. ([#116043](https://github.com/kubernetes/kubernetes/pull/116043), [@sanposhiho](https://github.com/sanposhiho)) [SIG API Machinery, Apps and Autoscaling]