}

func (a *Agent) DisableDraining() {
	a.skipDrain.Store(true)
}

func (a *Agent) DrainNow() {
	log.Infof("Agent draining proxy")
	err := a.proxy.Drain(true)
	if err != nil {
		log.Warnf("Error in invoking drain listeners endpoint: %v", err)
	}
	// If we drained now, skip draining + waiting later
	// When we terminate, we will instead exit immediately
	a.DisableDraining()
}

Onların kurslarını denemek isteyebilirsiniz:

* <a href="https://training.talkpython.fm/fastapi-courses" class="external-link" target="_blank">Talk Python Training</a>

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

namespace mlir::quant::stablehlo {

// Performs post-calibration graph transformation as part of post-training
// static-range quantization.
//
// The resulting `ModuleOp` contains quantized StableHLO ops serialized in
// `TF::XlaCallModuleOp`s. They are quantized using the statistics collected

  // CPU. Enable this layout optimization after we introduce TFRT native ops
  // for training.
  if (absl::StrContains(pass_options.default_device, "CPU")) {
    pass_options.skip_fold_transpose_in_ops = true;
  }
  pass_options.enable_optimizer = options.enable_optimizer;
  // Use TFRT TPU OpKernel for training.
  pass_options.target_tpurt = false;
  pass_options.tpu_use_core_selector = options.tpu_use_core_selector;

                         METHOD_DYNAMIC_RANGE_INT8,
                     "drq", "Post-training dynamic-range quantizaiton"),
          clEnumValN(tensorflow::quantization::QuantizationMethod::
                         METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8,
                     "weight_only", "Post-training weight_only quantizaiton"))};
};

class CheckQuantizableOps

    /**
     * Returns the value, failing if it has not been calculated.
     * Does not calculate the value on demand and does not block if the value is currently being calculated.
     *
     * <p>Rethrows any exception that happened while calculating the value</p>
     */
    T get() throws IllegalStateException;

    /**
     * Returns the result of calculating the value, failing if it has not been calculated.

#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace mlir::quant::stablehlo {

// Performs post-calibration graph transformation as part of post-training
// static-range quantization.
//
// The resulting `ModuleOp` contains quantized StableHLO ops serialized in
// `TF::XlaCallModuleOp`s. They are quantized using the statistics collected

//RUN: tf_tfl_translate --post-training-quantization --enable-stablehlo-conversion --input-mlir --output-mlir %s -o - | FileCheck %s


module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>

			assert.Equal(t, tt.want, calculateCPUMaximum(&tt.cpuLimit, tt.cpuCount))
		})
	}
}

func TestCalculateWindowsResources(t *testing.T) {
	// TODO: remove skip once the failing test has been fixed.
	t.Skip("Skip failing test on Windows.")

	_, _, fakeRuntimeSvc, err := createTestRuntimeManager()
	require.NoError(t, err)

	tests := []struct {
		name     string
		cpuLim   resource.Quantity

	"k8s.io/kubernetes/pkg/volume/util/hostutil"
	"k8s.io/kubernetes/pkg/volume/util/subpath"
)

func TestMakeMountsWindows(t *testing.T) {
	// TODO: remove skip once the failing test has been fixed.
	t.Skip("Skip failing test on Windows.")
	container := v1.Container{
		VolumeMounts: []v1.VolumeMount{
			{
				MountPath: "c:/etc/hosts",
				Name:      "disk",
				ReadOnly:  false,
			},
			{