ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  } else if (!async) {
    // Set the local device to CPU to easily validate mirroring
    string cpu_device_name;
    ASSERT_TRUE(GetDeviceName(ctx, &cpu_device_name, "CPU"));
    TFE_OpSetDevice(matmul, cpu_device_name.c_str(), status);
    EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    auto remote_arg =

	if len(udids) == 0 {
		fail("no udid found; is a device connected?")
	}

	mps := detectMobileProvisionFiles(udids)
	if len(mps) == 0 {
		fail("did not find mobile provision matching device udids %q", udids)
	}

	fmt.Println("# Available provisioning profiles below.")
	fmt.Println("# NOTE: Any existing app on the device with the app id specified by GOIOS_APP_ID")

	}
	// Failure case where we detected successfully cross device mounts.
	{
		absPaths := []string{"/path/to/1"}
		if err = checkCrossDevice(absPaths, mountsPath); err == nil {
			t.Fatal("Expected to fail, but found success")
		}

		mp := []mountInfo{
			{"/dev/2", "/path/to/1/2", "type2", []string{"flags"}, "2", "2"},
		}

    if _os.path.exists(_plugin_dir):
      _ll.load_library(_plugin_dir)
      # Load Pluggable Device Library
      _ll.load_pluggable_device_library(_plugin_dir)

if _os.getenv("TF_PLUGGABLE_DEVICE_LIBRARY_PATH", ""):
  _ll.load_pluggable_device_library(
      _os.getenv("TF_PLUGGABLE_DEVICE_LIBRARY_PATH")
  )

# Add Keras module aliases

  const char remote_device_name[] =
      "/job:localhost/replica:0/task:1/device:CPU:0";
  auto* h0_task1 =
      TFE_TensorHandleCopyToDevice(h0_task0, ctx, remote_device_name, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  auto* h1_task1 =
      TFE_TensorHandleCopyToDevice(h1_task0, ctx, remote_device_name, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

};
using OwnedTFBuffer = std::unique_ptr<TF_Buffer, TFBufferDeleter>;

class CGraphOptimizer : public CustomGraphOptimizer {
 public:
  explicit CGraphOptimizer(TP_Optimizer optimizer, const char* device_type)
      : optimizer_(optimizer), device_type_(device_type) {
    if (optimizer.create_func != nullptr) {
      c_optimizer_ = (*optimizer_.create_func)();
    } else {
      c_optimizer_ = nullptr;
    }
  }

                                    Tensor* tensor);

absl::StatusOr<xla::PjRtCApiClient*> GetPjRtCApiClient(
    const DeviceType& device_type);

absl::Status ResetPjRtClient(const DeviceType& device_type);

}  // namespace tensorflow

    const DeviceType& device_type) {
  TF_ASSIGN_OR_RETURN(tsl::StatusOr<xla::PjRtClient*> pjrt_client,
                      tensorflow::GetPjRtClient(device_type));
  auto* pjrt_c_api_client = dynamic_cast<xla::PjRtCApiClient*>(*pjrt_client);
  if (pjrt_c_api_client == nullptr) {
    return absl::InternalError(absl::StrCat("PjRtClient for ",
                                            device_type.type_string(),

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

      encodings[ 0xf] = encoding // Shift In
      encodings[ 0x10] = encoding // Data Link Escape
      encodings[ 0x11] = encoding // Device Control 1 (oft. XON)
      encodings[ 0x12] = encoding // Device Control 2
      encodings[ 0x13] = encoding // Device Control 3 (oft. XOFF)
      encodings[ 0x14] = encoding // Device Control 4
      encodings[ 0x15] = encoding // Negative Acknowledgment
      encodings[ 0x16] = encoding // Synchronous idle