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

- 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.