onnxruntime

Eager mode: implement resize_ operation (#12004)

Add support for PyTorch `resize_` operation. The PyTorch API method is documented
here:

https://pytorch.org/docs/stable/generated/torch.Tensor.resize_.html

Implementation notes:

There are some implementation details that might deviate from
expectations:

  - As the Onnxruntime::tensor does not support resize operation, this
    functionality is supported on the TensorImpl by swapping out the
    backing tensor if the size changes.

  - In the ORT model the shape of the TensorImpl is defined by the
    backing onnxruntime::tensor, so it is not supported to have a
    TensorImpl with a different shape / size than the backing
    onnxruntime::tensor. This means when resizing to a smaller TensorImpl,
    other implementations might keep the same backing storage, ORT will
    re-allocate a new onnxruntime::tensor and copy over as many of the
    existing elements that fit. Functionally, you will end up with same
    output, but the underlying buffer will be re-allocated.

    A future change could be to allow ORTTensorImpl to have a different
    size / shape than the onnxrutime::tensor backing it, and then we
    could improve this behavior.

 The canonical CPU / CUDA implementations in PyTorch repository:
     CPU: aten/src/ATen/native/Resize.cpp
     CUDA: aten/src/ATen/native/cuda/Resize.cpp