4.0b2: Flexible shapes not working but enumerated image sizes work

## 🐞Bug
I have successfully converted a model to use images instead of multi-arrays (with 4.0b2).

I can add flexible shapes and the model compiles/exports fine. If I run predict on the resulting mode with an input image with a dimension that is not the fixed dimension (and inside the flexible shape range), I get the trace below:

## Trace
> Traceback (most recent call last):
> File "/Users/jeshua/produceImageWithMLModel.py", line 36, in
> main()
> File "/Users/jeshua/produceImageWithMLModel.py", line 30, in main
> outputImage = model.predict({args.inputLayer: image})[args.outputLayer]
> File "/Users/jeshua/coremltools4/lib/python3.8/site-packages/coremltools/models/model.py", line 329, in predict
> return self.__proxy__.predict(data, useCPUOnly)
> RuntimeError: {
> NSLocalizedDescription = "Error binding image input buffer input.";

## Code snippet
I added the flexible shapes like this:
```
img_size_ranges = flexible_shape_utils.NeuralNetworkImageSizeRange()
img_size_ranges.add_height_range((64, 4096))
img_size_ranges.add_width_range((64, 4096))

flexible_shape_utils.update_image_size_range(spec, feature_name='input', size_range=img_size_ranges)
flexible_shape_utils.update_image_size_range(spec, feature_name='output', size_range=img_size_ranges)
```

If I add enumerated shapes, the enumerated shapes work (only):

```
image_sizes = [flexible_shape_utils.NeuralNetworkImageSize(512, 512)]
image_sizes.append(flexible_shape_utils.NeuralNetworkImageSize(1024, 1024))
image_sizes.append(flexible_shape_utils.NeuralNetworkImageSize(2048, 2048))
flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='input', sizes=image_sizes)
flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='output', sizes=image_sizes)
```

## System environment:
- coremltools version 4.0b2
- OS both MacOS, Linux
- macOS version 10.15.5
- virtualenv
- python version 3.6

Reproduce the failure using the flexible_shape_utils calls in the issue and an in-range image size that is not fixed. Start by tracing image input binding during model.predict; done means flexible image ranges work on both input and output without the reported binding error, while enumerated sizes remain supported.

Written by the indexing model from the issue text.