#     elinux_aarch64:  Embedded Linux options for aarch64 (ARM64) CPU support.
#     elinux_armhf:    Embedded Linux options for armhf (ARMv7) CPU support.
#
# Release build options (for all operating systems)
#     release_base:                    Common options for all builds on all operating systems.
#     release_cpu_linux:               Toolchain and CUDA options for Linux CPU builds.

)

config_setting(
    name = "arm",
    values = {"cpu": "arm"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "armeabi",
    values = {"cpu": "armeabi"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "armeabi-v7a",
    values = {"cpu": "armeabi-v7a"},
    visibility = ["//visibility:public"],
)

config_setting(

    // .backing_device of shape is CPU since the tensor is backed by CPU
    backing_device_name =
        TFE_TensorHandleBackingDeviceName(retvals[0], status.get());
    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
    ASSERT_TRUE(absl::StrContains(backing_device_name, "CPU:0"))
        << backing_device_name;

    TFE_DeleteOp(shape_op);

		t.Run(testCase.name, func(t *testing.T) {
			// Hack cpuid to the CPU doesn't appear to support AVX2.
			// Restore whatever happens.
			if cpuid.CPU.Supports(cpuid.AVX2) {
				cpuid.CPU.Disable(cpuid.AVX2)
				defer cpuid.CPU.Enable(cpuid.AVX2)
			}
			if simdjson.SupportedCPU() {
				t.Fatal("setup error: expected cpu to be unsupported")
			}
			testReq := testCase.requestXML
			if len(testReq) == 0 {

* gce/kube-down: Parallelize IGM deletion, batch more ([#27302](https://github.com/kubernetes/kubernetes/pull/27302), [@zmerlynn](https://github.com/zmerlynn))
* Enable dynamic allocation of heapster/eventer cpu request/limit ([#27185](https://github.com/kubernetes/kubernetes/pull/27185), [@gmarek](https://github.com/gmarek))

        TF_NewOperation(host_graph_, func_name_, func_node_name_);
    for (auto input : inputs) {
      TF_AddInput(desc, input);
    }
    // Set device to CPU because some ops inside the function might not be
    // available on GPU.
    TF_SetDevice(desc, "/cpu:0");
    *op = TF_FinishOperation(desc, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    ASSERT_NE(*op, nullptr);
  }

  FunctionDef fdef() {

          "interval": "",
          "legendFormat": "{{server}}",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "CPU Usage",
      "type": "gauge"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_DeleteGraph(graph);
  TF_DeleteStatus(s);
}

// If `device` is non-empty, run Min op on that device.
// Otherwise run it on the default device (CPU).
void RunMinTest(const string& device, bool use_XLA) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Make a placeholder operation.
  TF_Operation* feed = Placeholder(graph, s);

	if err != nil {
		return nil, grid.NewRemoteErr(err)
	}
	info := getLocalServerProperty(globalEndpoints, &r, metrics)
	return madminServerProperties.NewJSONWith(&info), nil
}

// GetCPUsHandler - returns CPU info.
func (s *peerRESTServer) GetCPUsHandler(_ *grid.MSS) (*grid.JSON[madmin.CPUs], *grid.RemoteErr) {
	info := madmin.GetCPUs(context.Background(), globalLocalNodeName)
	return madminCPUs.NewJSONWith(&info), nil
}