New to PyTorch. Getting a segfault on Jetson NX after call to torch::nn:functional::interpolate() and could use some hints.  Code is similar to this:
auto options1 = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA).requires_grad(false);
auto t = torch::from_blob(ptr, {4, 34, 160, 160}, options1);

// code to populate tensor here ...

// then when it comes time to interpolate, this is what I'm trying to run:
const auto options2 = torch::nn::functional::InterpolateFuncOptions().size(std::vector<int64_t>({320,320})).mode(torch::kBilinear).align_corners(false);
auto resized_output = torch::nn::functional::interpolate(tensors[1], options2);

// now how do we get access to the data without segfaulting?
float * new_tensor_ptr = reinterpret_cast<float*>(resized_output.data_ptr());
std::cout << "first byte of new tensor: " << new_tensor_ptr[0] << std::endl; // <-- this segfaults