image input + rangeDim doesn't work!

https://github.com/xuebinqin/U-2-Net
I want to support flexible input, output(with image type, not multiarray) but it doesn't work.

## Trace
ValueError: Unable to map torch_upsample_bilinear to core upsample

## To Reproduce

net = U2NETP(3,1)
net.load_state_dict(torch.load('u2netp.pth', map_location='cpu'))
net.cpu()
net.eval()

example_input = torch.rand(1, 3, 320, 320) * 255
input_shape = ct.Shape(shape=(1, 3, ct.RangeDim(1, 320, default=320), ct.RangeDim(1, 320, default=320)))

traced_model = torch.jit.trace(net, example_input)
model = ct.convert(traced_model, inputs=[ct.ImageType( shape= input_shape)], convert_to='neuralnetwork')

Here are used python model and code.
[u2netp.pth.zip](https://github.com/apple/coremltools/files/6882291/u2netp.pth.zip)
[model.zip](https://github.com/apple/coremltools/files/6882295/model.zip)

## System environment
- coremltools version (e.g., 3.0b5): https://gitlab.com/zach_nation/coremltools/-/pipelines/339060573, 4.1, 5.0b1, 5.0b2
- OS (e.g., MacOS, Linux): Mac OS
- macOS version (if applicable): Monterey, Big Sur
- XCode version (if applicable): Xcode 13, 12
- How you install python (anaconda, virtualenv, system): system
- python version (e.g. 3.7): 3.8

Start by reproducing the provided U2NETP example with the traced model, ct.ImageType, ct.Shape, and ct.RangeDim inputs. Investigate the conversion path that reports “Unable to map torch_upsample_bilinear to core upsample,” using the linked model archives. Done means conversion succeeds with flexible image dimensions and produces the requested image-type output rather than a multiarray.

Written by the indexing model from the issue text.