TEST(DLPack, HandleFromDLPackStrides) {
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  TestHandleFromDLPack(status, ctx, {}, {});
  TestHandleFromDLPack(status, ctx, {4}, {});
  TestHandleFromDLPack(status, ctx, {4}, {1});

  if (TF_GetCode(status) != TF_OK) return;
  if (!grad) return;

  status->status = g->graph.AddFunctionDef(grad->record->fdef(),
                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());

  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }

  std::vector<int64_t> dev_dims = TensorShapeAsVector(*handle, &status->status);
  if (!status->status.ok()) {
    return nullptr;
  }
  return new TFE_TensorDebugInfo(dev_dims);
}

TF_CAPI_EXPORT extern void TFE_DeleteTensorDebugInfo(
    TFE_TensorDebugInfo* debug_info) {
  delete debug_info;
}

  }
}

void* TFE_HandleToDLPack(TFE_TensorHandle* h, TF_Status* status) {
  auto tf_dlm_context = GetDlContext(h, status);
  if (!status->status.ok()) {
    return nullptr;
  }

  auto* tf_dlm_data = TFE_TensorHandleDevicePointer(h, status);
  if (!status->status.ok()) {
    return nullptr;
  }

  const Tensor* tensor = GetTensorFromHandle(h, status);
  TF_DataType data_type = static_cast<TF_DataType>(tensor->dtype());

absl::Status SetAttrBool(AbstractOperation*, const char* attr_name, bool value,
                         ForwardOperation*);
absl::Status SetAttrType(AbstractOperation*, const char* attr_name,
                         DataType value, ForwardOperation*);
absl::Status SetAttrShape(AbstractOperation*, const char* attr_name,
                          const int64_t* dims, const int num_dims,
                          ForwardOperation*);

      // getName is final on Thread, no need to worry about exceptions
      result = "running=[RUNNING ON " + ((Thread) state).getName() + "]";
    } else {
      result = "running=[NOT STARTED YET]";
    }
    return result + ", " + toPendingString();
  }

  abstract String toPendingString();

		if status.Summary == "finished" {
			fmt.Println("Healstatus on items ===")
			for _, item := range status.Items {
				if err = enc.Encode(&item); err != nil {
					log.Fatalln(err)
				}
			}
			break
		}
		if status.Summary == "stopped" {
			fmt.Println("Healstatus on items ===")
			fmt.Println("Heal failed with", status.FailureDetail)
			break
		}

		for _, item := range status.Items {

automatically on GitHub.

If you want to contribute, start working through the TensorFlow codebase,
navigate to the
[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.

   * @since 8.0
   */
  public static final byte SOH = 1;

  /**
   * Start of Text: A communication control character which precedes a sequence of characters that
   * is to be treated as an entity and entirely transmitted through to the ultimate destination.
   * Such a sequence is referred to as "text." STX may be used to terminate a sequence of characters
   * started by SOH.
   *
   * @since 8.0
   */
  public static final byte STX = 2;

  virtual absl::Status SetDeviceName(const char* name) = 0;

  virtual absl::Status AddInput(AbstractTensorHandle* input) = 0;
  virtual absl::Status AddInputList(
      absl::Span<AbstractTensorHandle* const> inputs) = 0;
  virtual absl::Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                               int* num_retvals) = 0;