pytorch-3dunet_vaiq_int8 model support

[AmosLewis]

Failed ops in mlir patch pytorch-3dunet_vaiq_int8.default.onnx.torch.elide.mlir

• [e2e][onnx][model] onnx.Resize [(half_pixel, linear), (asymmetric, nearest)] SHARK-ModelDev#616

%1777 = torch.operator "onnx.Resize"(%1716, %none, %none, %1776) {torch.onnx.coordinate_transformation_mode = "asymmetric", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "nearest", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[?,256,?,?,?],f32>, !torch.none, !torch.none, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32>

%2262 = torch.operator "onnx.Resize"(%2201, %none, %none, %2261) {torch.onnx.coordinate_transformation_mode = "asymmetric", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "nearest", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[?,128,?,?,?],f32>, !torch.none, !torch.none, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32>

%2747 = torch.operator "onnx.Resize"(%2686, %none, %none, %2746) {torch.onnx.coordinate_transformation_mode = "asymmetric", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "nearest", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[?,64,?,?,?],f32>, !torch.none, !torch.none, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32>

[AmosLewis]

@aldesilv With iree-compiler bump torch-mlir to onnx.resize patch iree-org/iree#17358:
new error:

pytorch-3dunet_vaiq_int8.default.onnx.torch.mlir:503:12: error: 'tensor.cast' op operand type 'tensor<1x256x?x?x16xf32>' and result type 'tensor<1x256x16x32x32xf32>' are cast incompatible
    %461 = torch.aten.__interpolate.size_list_scale_list %459, %460, %none, %str, %false, %none, %false : !torch.vtensor<[1,256,8,16,16],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[1,256,16,32,32],f32>
           ^
pytorch-3dunet_vaiq_int8.default.onnx.torch.mlir:503:12: note: see current operation: %3778 = "tensor.cast"(%3777) : (tensor<1x256x?x?x16xf32>) -> tensor<1x256x16x32x32xf32>