return ConstantFoldOpIfPossible(reshape_op).front();
}

// Checks if a value can be symmetrically quantized.
bool CanBeSymmetricallyQuantized(Value weight) {
  auto dq_op = weight.getDefiningOp<quantfork::DequantizeCastOp>();
  if (!dq_op) return true;

  auto qtype =
      mlir::cast<TensorType>(dq_op.getArg().getType()).getElementType();

		// We don't have a great way to do it. TODO: add a fault injection API for TCP?
		return &istio.TCPRoute{
			Route: []*istio.RouteDestination{{
				Destination: &istio.Destination{
					Host:   "internal.cluster.local",
					Subset: "zero-weight",
					Port:   &istio.PortSelector{Number: 65535},
				},
				Weight: 0,
			}},
		}, nil
	}

/**
 * Generates an HTML dependency report. This report
 * combines the features of the ASCII dependency report and those of the ASCII
 * dependency insight report. For a given project, it generates a tree of the dependencies
 * of every configuration, and each dependency can be clicked to show the insight of
 * this dependency.
 * <p>
 * This task generates a report for the task's containing project by default. But it can also generate

			t.Fatalf("for type %T: got %q; want %q", dst, err, want)
		}
	}
}

func TestNoFixedSize(t *testing.T) {
	type Person struct {
		Age    int
		Weight float64
		Height float64
	}

	person := Person{
		Age:    27,
		Weight: 67.3,
		Height: 177.8,
	}

	for _, enc := range encoders {
		t.Run(enc.name, func(t *testing.T) {
			_, err := enc.fn(LittleEndian, &person)
			if err == nil {

// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-custom-op-quantization=CustomTestOp=1" -tfl-quantize="enable-dynamic-range-quantization=true enable-custom-op-weight-only=CustomTestOp=false" -tfl-post-quantize="enable-no-side-effect=CustomTestOp=true" | FileCheck...

-->
We would like to thank the following community members for their contributions to this release of Gradle:

Be sure to check out the [public roadmap](https://blog.gradle.org/roadmap-announcement) for insight into what's planned for future releases.

## Upgrade instructions

Switch your build to use Gradle @version@ by updating your wrapper:

`./gradlew wrapper --gradle-version=@version@`

// makeImg allocates and initializes the destination image.
func (d *decoder) makeImg(mxx, myy int) {
	if d.nComp == 1 {
		m := image.NewGray(image.Rect(0, 0, 8*mxx, 8*myy))
		d.img1 = m.SubImage(image.Rect(0, 0, d.width, d.height)).(*image.Gray)
		return
	}

	h0 := d.comp[0].h
	v0 := d.comp[0].v
	hRatio := h0 / d.comp[1].h
	vRatio := v0 / d.comp[1].v
	var subsampleRatio image.YCbCrSubsampleRatio
	switch hRatio<<4 | vRatio {

	{Name: string(v1.ResourceCPU), Weight: 1},
	{Name: string(v1.ResourceMemory), Weight: 1},
	{Name: string(v1.ResourcePods), Weight: 1},
	{Name: string(v1.ResourceStorage), Weight: 1},
	{Name: string(v1.ResourceEphemeralStorage), Weight: 1},
	{Name: string(extendedResourceA), Weight: 1},
	{Name: string(extendedResourceB), Weight: 1},
	{Name: string(kubernetesIOResourceA), Weight: 1},

    <a href="https://maven.apache.org/plugins/">plugins</a> to utilise a
    number of other development tools for reporting or the build
    process.</p>

      <p>
        <object data="images/maven-deps.svg" width="968" height="605"></object>
      </p>
    </section>

  </body>

#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_options.pb.h"

namespace mlir::quant::stablehlo {

// Creates a pass that quantizes weight component of StableHLO graph.
std::unique_ptr<OperationPass<func::FuncOp>> CreateQuantizeWeightPass(
    const ::stablehlo::quantization::QuantizationComponentSpec&
        quantization_component_spec = {});