amd64 binary instead of the go-runner that matches that architecture.
  
  This commit explicitly sets the '--platform=linux/${arch}' to ensure
  we're pulling the correct go-runner arch from the manifest list.
  
  Before:
  `FROM ${base_image}`
  
  After:
  `FROM --platform=linux/${arch} ${base_image}` ([#94613](https://github.com/kubernetes/kubernetes/pull/94613), [@justaugustus](https://github.com/justaugustus)) [SIG Release]

				continue
			}
			y := left.Args[1]
			x := left.Args[0]
			right := v_1
			if right.Op != OpRsh16Ux64 {
				continue
			}
			_ = right.Args[1]
			if x != right.Args[0] {
				continue
			}
			right_1 := right.Args[1]
			if right_1.Op != OpSub64 {
				continue
			}
			_ = right_1.Args[1]
			right_1_0 := right_1.Args[0]

			break
		}
		y := v_2.Args[1]
		if ptr != v_2.Args[0] {
			break
		}
		v.reset(OpAMD64ADDL)
		v0 := b.NewValue0(v_2.Pos, OpAMD64MOVLf2i, typ.UInt32)
		v0.AddArg(y)
		v.AddArg2(x, v0)
		return true
	}
	return false
}
func rewriteValueAMD64_OpAMD64ADDLmodify(v *Value) bool {
	v_2 := v.Args[2]
	v_1 := v.Args[1]
	v_0 := v.Args[0]

}
func rewriteValueARM64_OpARM64CSINC(v *Value) bool {
	v_2 := v.Args[2]
	v_1 := v.Args[1]
	v_0 := v.Args[0]
	// match: (CSINC [cc] x y (InvertFlags cmp))
	// result: (CSINC [arm64Invert(cc)] x y cmp)
	for {
		cc := auxIntToOp(v.AuxInt)
		x := v_0
		y := v_1
		if v_2.Op != OpARM64InvertFlags {
			break
		}
		cmp := v_2.Args[0]
		v.reset(OpARM64CSINC)
		v.AuxInt = opToAuxInt(arm64Invert(cc))

	fldPath := field.NewPath("test")
	type args struct {
		probe   *core.Probe
		fldPath *field.Path
	}
	tests := []struct {
		name string
		args args
		want field.ErrorList
	}{{
		args: args{
			probe:   &core.Probe{},
			fldPath: fldPath,
		},
		want: field.ErrorList{field.Required(fldPath, "must specify a handler type")},
	}, {
		args: args{
			probe: &core.Probe{

  }];

  let arguments = (ins
    Arg<TF_Float32Tensor, [{A 4D Tensor for input data.}]>:$x,
    Arg<TF_Float32Tensor, [{A 1D Tensor for scaling factor, to scale the normalized x.}]>:$scale,
    Arg<TF_Float32Tensor, [{A 1D Tensor for offset, to shift to the normalized x.}]>:$offset,
    Arg<TF_Float32Tensor, [{A 1D Tensor for population mean. Used for inference only;

to the Sine of Refraction as I to R. Now, if you suppose the Point of
Incidence N to move from the Point B, continually till it come to L, the
Arch QF will first increase and then decrease, and so will the Angle AXR
which the Rays AN and GR contain; and the Arch QF and Angle AXR will be
biggest when ND is to CN as sqrt(II - RR) to sqrt(3)RR, in which
case NE will be to ND as 2R to I. Also the Angle AYS, which the Rays AN

    *   Adding center bias option for boosted trees.
*   Add `synchronization` and `aggregation` args to get_variable(). These args
    will be used for distributed variables.
*   Add `synchronization` and `aggregation` args to the layer `add_weight()`
    API. These args will be used for distributed variables.
*   `tf.losses.*` do not add to the global collection when executing eagerly (to