struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

  explicit TfDlManagedTensorCtx(const TensorReference& ref) : reference(ref) {}
};

// Gets tensor from eager tensor handle.
const Tensor* GetTensorFromHandle(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {

AbstractTensorHandle* TapeTensor::ZerosLike() const { return nullptr; }

class TapeVSpace
    : public eager::VSpace<AbstractTensorHandle, GradientFunction, TapeTensor> {
 public:
  explicit TapeVSpace(AbstractContext* ctx) : ctx_(ctx) {}
  ~TapeVSpace() override {}

  // Returns the number of elements in the gradient tensor.
  int64_t NumElements(AbstractTensorHandle* tensor) const override;

    grad_inputs[0]->Ref();
    grad_inputs[1]->Ref();
    return absl::OkStatus();
  }
  ~AddGradientFunction() override {}
};

class ExpGradientFunction : public GradientFunction {
 public:
  explicit ExpGradientFunction(AbstractTensorHandle* exp) : exp_(exp) {
    exp->Ref();
  }
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,

  // otherwise be the name of this custom device. Ops are placed onto a custom
  // device if any of their inputs are on that custom device, but custom devices
  // are free to set a bad status in order to require explicit placement.
  void (*execute)(const TFE_Op* op, int* num_outputs,
                  TFE_TensorHandle** outputs, TF_Status* s, void* device_info);

  // Method to delete a device.

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.
class GraphTensor : public TracingTensorHandle {
 public:
  explicit GraphTensor(TF_Output output, TF_Graph* graph)
      : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}

  tensorflow::DataType DataType() const override {

// LINT.IfChange
// Note: Keep in sync with exported copy of enum in eager/c_api.h.
enum ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  DEVICE_PLACEMENT_WARN = 1,
  // Silently copy the tensor, which has a performance cost since the operation

  // functions (and hence the tensors they keep alive). Instead, everything
  // is deleted in ~GradientTape. Persistent GradientTapes are useful when
  // users want to compute multiple gradients over the same tape.
  explicit GradientTape(bool persistent) : persistent_(persistent) {}
  ~GradientTape() {
    for (const auto& pair : op_tape_) {
      pair.second.backward_function_deleter(pair.second.backward_function);
    }
  }

  return result_store_links


def indent_xml(elem, level=0) -> None:
  """Indents and newlines the XML for better output."""
  indent_str = '\n' + level * '  '
  if len(elem):  # pylint: disable=g-explicit-length-test  # `if elem` not valid
    if not elem.text or not elem.text.strip():
      elem.text = indent_str + '  '
    if not elem.tail or not elem.tail.strip():
      elem.tail = indent_str
    for elem in elem:

// multiple DeviceThreads should always be accessed in the same order to avoid
// deadlocks.
class DeviceThread {
 public:
  // Starts a background thread waiting for `StartExecute`.
  explicit DeviceThread(const std::string& device, const bool is_async,
                        const int in_flight_nodes_limit)
      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in

  TFE_ContextOptionsSetAsync(opts, static_cast<unsigned char>(async));
  TFE_ContextOptionsSetDevicePlacementPolicy(opts,
                                             TFE_DEVICE_PLACEMENT_EXPLICIT);
  TFE_Context* ctx = TFE_NewContext(opts, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);