in_x);
    auto out_h_f32 = rewriter.create<CastOp>(
        loc, tensorflow::GetTypeFromTFTensorShape({}, rewriter.getF32Type()),
        out_x);

    Value x_scale = rewriter.create<DivOp>(
        loc, tensorflow::GetTypeFromTFTensorShape({}, rewriter.getF32Type()),
        in_x_f32, out_h_f32);

    Value x_range = rewriter.create<RangeOp>(
        loc,

// TF-DAG: %[[arg_quantized:.*]] = "tf.PartitionedCall"(%arg0, %[[in_scale]], %[[in_out_zp]]) <{config = "", config_proto = "", executor_type = "", f = @quantize_i8}>

    x_shape = [label_to_size.get(x_label) for x_label in x_labels]
    y_shape = [label_to_size.get(y_label) for y_label in y_labels]
    bias_shape = None
    if use_bias:
      bias_shape = [label_to_size.get(out_label) for out_label in out_labels]
      bias_shape = bias_shape[-1:]
    contracting_dims = set()

    x_signature = list(x_shape)
    y_signature = list(y_shape)

)

// +genclient
// +genclient:method=GetScale,verb=get,subresource=scale,result=k8s.io/api/autoscaling/v1.Scale
// +genclient:method=UpdateScale,verb=update,subresource=scale,input=k8s.io/api/autoscaling/v1.Scale,result=k8s.io/api/autoscaling/v1.Scale
// +genclient:method=ApplyScale,verb=apply,subresource=scale,input=k8s.io/api/autoscaling/v1.Scale,result=k8s.io/api/autoscaling/v1.Scale
// +k8s:deepcopy-gen:interfaces=k8s.io/apimachinery/pkg/runtime.Object

		q3.SetScaled(item.value, item.scale)
		if q.Cmp(*q3) != 0 {
			t.Errorf("Expected %v and %v to be equal", q, q3)
		}
	}
}

func TestScaledValue(t *testing.T) {
	table := []struct {
		fromScale Scale
		toScale   Scale
		expected  int64
	}{
		{Nano, Nano, 1},
		{Nano, Micro, 1},
		{Nano, Milli, 1},
		{Nano, 0, 1},
		{Micro, Nano, 1000},
		{Micro, Micro, 1},

	for _, successCase := range successCases {
		if errs := ValidateScale(&successCase); len(errs) != 0 {
			t.Errorf("expected success: %v", errs)
		}
	}

	errorCases := []struct {
		scale autoscaling.Scale
		msg   string
	}{{
		scale: autoscaling.Scale{
			ObjectMeta: metav1.ObjectMeta{
				Name:      "frontend",
				Namespace: metav1.NamespaceDefault,
			},
			Spec: autoscaling.ScaleSpec{
				Replicas: -1,
			},
		},

func.func @selu(%arg0: tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xf32> {
    // CHECK-DAG:   %[[ZERO:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
    // CHECK-DAG:   %[[SCALE:.*]] = "tf.Const"() <{value = dense<1.05070102> : tensor<f32>}> : () -> tensor<f32>
    // CHECK-DAG:   %[[SCALED_ALPHA:.*]] = "tf.Const"() <{value = dense<1.75809932> : tensor<f32>}> : () -> tensor<f32>

  //
  // For example:
  // 'pods' means pods.
  // 'pods/log' means the log subresource of pods.
  // '*' means all resources, but not subresources.
  // 'pods/*' means all subresources of pods.
  // '*/scale' means all scale subresources.
  // '*/*' means all resources and their subresources.
  //
  // If wildcard is present, the validation rule will ensure resources do not
  // overlap with each other.
  //

- 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,...

          'same_scale_op': (
              'reshape',  # This corresponds to stablehlo.dynamic_reshape
              'slice',  # This corresponds to stablehlo.dynamic_slice.
              # TODO: b/326242075 - Support other same-scale ops.
          ),
          'dim_sizes': (([None, 1024], [1024, 3]),),
      }])
  )
  @test_util.run_in_graph_and_eager_modes
  def test_matmul_and_same_scale_ptq_model_dynamic(
      self,