}
	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
}

# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX
# is forward compatible beyond the current compute capability major
# release while SASS is only forward compatible inside the current
# major release. Example: sm_80 kernels can run on sm_89 GPUs but
# not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.

				"application/vnd.criticaltools.wbs+xml",
				"application/vnd.ctc-posml",
				"application/vnd.ctct.ws+xml",
				"application/vnd.cups-pdf",
				"application/vnd.cups-postscript",
				"application/vnd.cups-ppd",
				"application/vnd.cups-raster",
				"application/vnd.cups-raw",
				"application/vnd.curl.car",
				"application/vnd.curl.pcurl",
				"application/vnd.cybank",
				"application/vnd.data-vision.rdz",

* Use local disk for ConfigMap volume instead of tmpfs ([#25306](https://github.com/kubernetes/kubernetes/pull/25306), [@pmorie](https://github.com/pmorie))
* Alpha support for scheduling pods on machines with NVIDIA GPUs whose kubelets use the `--experimental-nvidia-gpus` flag, using the alpha.kubernetes.io/nvidia-gpu resource  ([#24836](https://github.com/kubernetes/kubernetes/pull/24836), [@therc](https://github.com/therc))

    values = {
        "apple_platform_type": "macos",
        "cpu": "darwin",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "macos_arm64_with_framework_shared_object",
    define_values = {
        "framework_shared_object": "true",
    },
    values = {
        "apple_platform_type": "macos",
        "cpu": "darwin_arm64",
    },
    visibility = ["//visibility:public"],

        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}"
      },

# Number of threads for hot thread monitoring.
crawler.hotthread.threads=3
# Timeout for hot thread monitoring (e.g., 30s).
crawler.hotthread.timeout=30s
# Type of hot thread monitoring (e.g., cpu).
crawler.hotthread.type=cpu
# Metadata fields to exclude from document content.
crawler.metadata.content.excludes=resourceName,X-Parsed-By,Content-Encoding.*,Content-Type.*,X-TIKA.*,X-FESS.*
# Mapping for document metadata names.

  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);

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