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);
      }
    }

		a.writeNameTo(w, abiDetail)

	case TYPE_MEM:
		a.WriteNameTo(w)
		if a.Index != REG_NONE {
			if a.Scale == 0 {
				// arm64 shifted or extended register offset, scale = 0.
				fmt.Fprintf(w, "(%v)", Rconv(int(a.Index)))
			} else {
				fmt.Fprintf(w, "(%v*%d)", Rconv(int(a.Index)), int(a.Scale))
			}
		}

	case TYPE_CONST:
		io.WriteString(w, "$")
		a.WriteNameTo(w)
		if a.Reg != 0 {

      This transformation pass modifies the input and output types of the function
      to what are specified. The task was not just adding cast operations, but,
      instead, using tfl.quantize and tfl.dequantize ops to scale the tensors.
  }];
  let constructor = "CreateModifyIONodesPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
  let options = [
      ListOption<"io_node_types_", "test-io-types", "std::string",

	unsafePoint       bool      // this op is an unsafe point, i.e. not safe for async preemption
	symEffect         SymEffect // effect this op has on symbol in aux
	scale             uint8     // amd64/386 indexed load scale
}

type inputInfo struct {
	idx  int     // index in Args array
	regs regMask // allowed input registers
}

type outputInfo struct {
	idx  int     // index in output tuple

  // continuing. When scaling down, the pods are removed in the opposite order.
  // The alternative policy is `Parallel` which will create pods in parallel
  // to match the desired scale without waiting, and on scale down will delete
  // all pods at once.
  // +optional
  optional string podManagementPolicy = 6;

  // updateStrategy indicates the StatefulSetUpdateStrategy that will be

{% block metas %}
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
{% endblock %}

<title>Simple Bookstore App</title>
{% block scripts %}
<script src="static/tailwind/tailwind.css"></script>
<script type="text/javascript">
  window.addEventListener("DOMContentLoaded", (event) => {
    const dialog = document.querySelector("dialog");

	// is likely to be the originator, so requeuing would hot-loop us.  Failures are requeued by the workqueue directly.
	// This is a low traffic and scale resource, so the copy is terrible.  It's not good, so better ideas
	// are welcome.
	oldCopy := oldCRD.DeepCopy()
	newCopy := newCRD.DeepCopy()
	oldCopy.ResourceVersion = ""
	newCopy.ResourceVersion = ""

        if (op->hasOneUse() &&
            op->user_begin()->hasTrait<OpTrait::IsTerminator>())
          return failure();
      }
      // If the quantize op is a requantize op, it is being used in other scale
      // adjustments and should be kept. Instead, moving dequantize op before
      // the requantize op to remove the unnecessary requantize op.
      if (auto qtype = quant::QuantizedType::getQuantizedElementType(

	return RegisterDefaults(scheme)
}

func SetDefaults_ResourceList(obj *v1.ResourceList) {
	for key, val := range *obj {
		// TODO(#18538): We round up resource values to milli scale to maintain API compatibility.
		// In the future, we should instead reject values that need rounding.
		const milliScale = -3
		val.RoundUp(milliScale)

		(*obj)[v1.ResourceName(key)] = val
	}
}

//
// It uses a brute-force recursive approach to enumerate all objects
// defined by dependencies of pkg, so that it can learn the set of
// package paths that may be mentioned in the fact encoding. This does
// not scale well; use [NewDecoderFunc] where possible.
func NewDecoder(pkg *types.Package) *Decoder {
	// Compute the import map for this package.
	// See the package doc comment.
	m := importMap(pkg.Imports())