// Remote device on `worker1`.
  const char remote_device[] = "/job:localhost/replica:0/task:1/device:CPU:0";
  // `ctx_0`, `ctx_1`, `ctx_2` contains `remote_device`.
  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_0);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  {

    TF_ImportGraphDefOptions* opts, const char* prefix);

// Set the execution device for nodes in `graph_def`.
// Only applies to nodes where a device was not already explicitly specified.
// `device` is copied and has no lifetime requirements.
TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsSetDefaultDevice(
    TF_ImportGraphDefOptions* opts, const char* device);

// Set whether to uniquify imported operation names. If true, imported operation

    CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    LOG(INFO) << "Device " << i << " has name " << device_name << ", type "
              << device_type;
    if (string(device_type) == DEVICE_GPU) {
      return device_name;
    }
  }
  // No GPU device found.
  return "";
}

string GPUDeviceName() {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(

                        {
                            "name": "device_ids",
                            "in": "query",
                            "required": True,
                            "schema": {
                                "type": "array",
                                "items": {"type": "integer"},
                                "title": "Device Ids",
                            },
                        }

  Returns:
    string of native cuda compute capabilities, separated by comma.
  """
  device_query_bin = os.path.join(
      environ_cp.get('CUDA_TOOLKIT_PATH'), 'extras/demo_suite/deviceQuery')
  if os.path.isfile(device_query_bin) and os.access(device_query_bin, os.X_OK):
    try:
      output = run_shell(device_query_bin).split('\n')
      pattern = re.compile('[0-9]*\\.[0-9]*')

    subclasses. We've restored that support!


## Version 3.13.0

_2019-02-04_

 *  **This release bumps our minimum requirements to Java 8+ or Android 5+.** Cutting off old
    devices is a serious change and we don't do it lightly! [This post][require_android_5] explains
    why we're doing this and how to upgrade.

)

# Config setting that is satisfied when CUDA device code should be compiled
# with clang. It does not imply that CUDA support has been enabled.
alias(
    name = "is_cuda_compiler_clang",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_compiler_clang",
        "@local_config_cuda//cuda:TRUE",
    ),
)

# Config setting that is satisfied when CUDA device code should be compiled

* Allow mounts to run in parallel for non-attachable volumes ([#28939](https://github.com/kubernetes/kubernetes/pull/28939), [@saad-ali](https://github.com/saad-ali))
* add enhanced volume and mount logging for block devices ([#24797](https://github.com/kubernetes/kubernetes/pull/24797), [@screeley44](https://github.com/screeley44))

        case 0x10B: // NT_STATUS_NOTIFY_CLEANUP:
        case NtStatus.NT_STATUS_NOTIFY_ENUM_DIR:
            break;
        case 0xC00000BB: // NT_STATUS_NOT_SUPPORTED
        case 0xC0000010: // NT_STATUS_INVALID_DEVICE_REQUEST
            throw new SmbUnsupportedOperationException();
        case NtStatus.NT_STATUS_PATH_NOT_COVERED:
            if ( ! ( req instanceof RequestWithPath ) ) {

              .on('click.daterangepicker', '...