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());

  auto tf_dlm_type = GetDlDataType(data_type, status);
  if (!status->status.ok()) {
    return nullptr;
  }

  TensorReference tensor_ref(*tensor);  // This will call buf_->Ref()
  auto* tf_dlm_tensor_ctx = new TfDlManagedTensorCtx(tensor_ref);
  tf_dlm_tensor_ctx->reference = tensor_ref;

  DLManagedTensor* dlm_tensor = &tf_dlm_tensor_ctx->tensor;
  dlm_tensor->manager_ctx = tf_dlm_tensor_ctx;
  dlm_tensor->deleter = &DLManagedTensorDeleter;
  dlm_tensor->dl_tensor.ctx = tf_dlm_context;
  int ndim = tensor->dims();
  dlm_tensor->dl_tensor.ndim = ndim;
  dlm_tensor->dl_tensor.data = tf_dlm_data;
  dlm_tensor->dl_tensor.dtype = tf_dlm_type;

  std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
  std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
  shape_arr->resize(ndim);
  stride_arr->resize(ndim, 1);
  for (int i = 0; i < ndim; i++) {
    (*shape_arr)[i] = tensor->dim_size(i);
  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];
  }

  dlm_tensor->dl_tensor.shape = shape_arr->data();
  // There are two ways to represent compact row-major data
  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();

  dlm_tensor->dl_tensor.byte_offset =
      0;  // TF doesn't handle the strides and byte_offsets here
  return static_cast<void*>(dlm_tensor);
}
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