{
    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());
  }

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_1);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),

  {
    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());
  }

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_1);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),

  TF_Status* status = TF_NewStatus();
  std::vector<std::string> device_names;
  TF_DeviceList* devices = TFE_ContextListDevices(ctx, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  const int num_devices = TF_DeviceListCount(devices);
  for (int i = 0; i < num_devices; ++i) {
    device_names.emplace_back(TF_DeviceListName(devices, i, status));

  }
  absl::Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }
  return absl::StrCat("TensorHandle(", value_string, ", shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()), ", device=\"",
                      device_name, "\")");
}

absl::Status ImmediateExecutionTensorHandle::SummarizeValue(

}
}  // namespace

extern "C" {

bool TFE_IsCustomDevice(TFE_Context* ctx, const char* device_name) {
  return tensorflow::unwrap(ctx)->IsCustomDevice(device_name);
}

void TFE_RegisterCustomDevice(TFE_Context* ctx, TFE_CustomDevice device,
                              const char* device_name, void* device_info,
                              TF_Status* status) {

                      std::unique_ptr<ParallelDevice> parallel_device)
      : device_name_(name), parallel_device_(std::move(parallel_device)) {}
  const std::string& name() const { return device_name_; }
  const ParallelDevice& device() const { return *parallel_device_; }

 private:
  std::string device_name_;
  std::unique_ptr<ParallelDevice> parallel_device_;
};

    return absl::OkStatus();
  }
  const string& Name() const override { return op_type_; }
  const string& DeviceName() const override { return device_name_; }

  absl::Status SetDeviceName(const char* name) override {
    // TODO(srbs): Implement this.
    device_name_ = name;
    return absl::OkStatus();
  }

  absl::Status AddInput(AbstractTensorHandle* input) override {

namespace tensorflow {
namespace parallel_device {

// Allocate a parallel device named `device_name` which forwards operations to
// `underlying_devices`, maintaining "parallel tensors" with components placed
// on each underlying device.
//
// For example if `device_name` is
//   "/job:localhost/replica:0/task:0/device:CUSTOM:0"
// and `underlying_devices` is

// `device_name`.
TFE_TensorHandle* CreateVarHandle(TFE_Context* ctx,
                                  const tensorflow::string& device_name,
                                  const tensorflow::string& variable_name);

// Create a variable with value `value` and name `variable_name` on a device
// with name `device_name`.
TFE_TensorHandle* CreateVariable(TFE_Context* ctx, float value,

      cpu_value.get(), context.get(), device_name, status.get()));
  EXPECT_EQ(TF_GetCode(status.get()), TF_UNIMPLEMENTED);

  std::array<TFE_TensorHandle*, 2> components{cpu_value.get(), cpu_value.get()};
  TensorHandlePtr device_value = CreatePerDeviceValues(
      context.get(), components, device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());