Click on a dependency and select the "Required By" tab to see the selection reason and origin of the dependency.

image::dependency-management-dependency-insight-report-build-scan.png[]

=== Dependency Insights

Gradle provides the built-in `dependencyInsight` task to render a  _dependency insight report_ from the command line.

func NewPreflightPhase() workflow.Phase {
	return workflow.Phase{
		Name:    "preflight [api-server-endpoint]",
		Short:   "Run join pre-flight checks",
		Long:    "Run pre-flight checks for kubeadm join.",
		Example: preflightExample,
		Run:     runPreflight,
		InheritFlags: []string{
			options.CfgPath,
			options.IgnorePreflightErrors,
			options.TLSBootstrapToken,

  %7 = "quantfork.dcast"(%6) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>
  %weight = arith.constant dense<1.0> : tensor<144x12xf32>
  %q_weight = "quantfork.qcast"(%weight) : (tensor<144x12xf32>) -> tensor<144x12x!quant.uniform<i8:f32, 0.074855112561992565:-1>>

		PrioritizeVerb: "prioritize",
		BindVerb:       "bar",
		Weight:         1,
	})

	validPlugins := validConfig.DeepCopy()
	validPlugins.Profiles[0].Plugins.Score.Enabled = append(validPlugins.Profiles[0].Plugins.Score.Enabled, config.Plugin{Name: "PodTopologySpread", Weight: 2})

	invalidPlugins := validConfig.DeepCopy()

limitations under the License.
==============================================================================*/

include "mlir/Pass/PassBase.td"

def QuantizeWeightPass : Pass<"stablehlo-quantize-weight", "mlir::func::FuncOp"> {
  let summary = "Quantizes the weight component of StableHLO graph.";
  let dependentDialects = ["mlir::stablehlo::StablehloDialect"];
  let constructor = "mlir::quant::stablehlo::CreateQuantizeWeightPass()";
}

		ret += edge.Weight
	}
	return ret
}

type edgeList []*Edge

func (el edgeList) Len() int {
	return len(el)
}

func (el edgeList) Less(i, j int) bool {
	if el[i].Weight != el[j].Weight {
		return abs64(el[i].Weight) > abs64(el[j].Weight)
	}

	from1 := el[i].Src.Info.PrintableName()
	from2 := el[j].Src.Info.PrintableName()
	if from1 != from2 {

func (e *badPathError) Error() string {
	return fmt.Sprintf("malformed tile path %q", e.path)
}

// A TileReader reads tiles from a go.sum database log.
type TileReader interface {
	// Height returns the height of the available tiles.
	Height() int

	// ReadTiles returns the data for each requested tile.
	// If ReadTiles returns err == nil, it must also return
	// a data record for each tile (len(data) == len(tiles))

          clEnumValN(tensorflow::quantization::QuantizationMethod::
                         METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8,
                     "weight_only", "Post-training weight-only quantizaiton"))};

  Option<bool> enable_per_channel_quantization_{
      *this, "enable-per-channel-quantization", llvm::cl::init(false),
      llvm::cl::desc("Whether enable per-channel quantized weights.")};
};

   * The maximum number of requests to execute concurrently. Above this requests queue in memory,
   * waiting for the running calls to complete.
   *
   * If more than [maxRequests] requests are in flight when this is invoked, those requests will
   * remain in flight.
   */
  var maxRequests = 64
    get() = this.withLock { field }
    set(maxRequests) {
      require(maxRequests >= 1) { "max < 1: $maxRequests" }
      this.withLock {

// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:     -stablehlo-quantize-composite-functions | FileCheck --check-prefix=CHECK %s

// Test that per-tensor weight-only quantized dot_general op is produced when
// empty `weight_only_ptq` is provided.

module attributes {tf_saved_model.semantics} {
  func.func private @quantize_dot_general_per_tensor(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {