Input shape ignored when converting from milinternal

## ❓Question

I am building a custom MIL program using the Python `Builder` class for MIL.
As stated in the coremltools API Reference I am starting my custom program as follows:

```python
import coremltools as ct
from coremltools.converters.mil import Builder as mb
@mb.program(input_specs=[mb.TensorSpec(shape=(1,3,640,640))])
def prog(a):
return mb.add(x=a, y=2)
```

How can I assign a flexible input/enumerated input shape to the program?

It seems that converting the program to a MLPackage using `ct.convert()` and using the attributes `inputs` and/or `outputs` do not affect the converted model. In the code snippet below I specifically renamed the input name to `new_a`. This code snippet will work just fine and will convert the program without any issues. However, if I open the MLProgram in Xcode and look at the Predictions tab I cannot see the changes applied to the input (see image bellow).

Is there a way to give the program a flexible input?

Full code sample:
```python
import coremltools as ct
from coremltools.converters.mil import Builder as mb

@mb.program(input_specs=[mb.TensorSpec(shape=(1,3,640,640))])
def prog(a):
return mb.add(x=a, y=2.0)

# Set the input_shape to use EnumeratedShapes.
input_shape = ct.EnumeratedShapes(shapes=[[1, 3, 640, 640],
[1, 3, 320, 320]],
default=[1, 3, 640, 640])
mlmodel = ct.convert(prog,
convert_to="mlprogram",
inputs=[ct.TensorType(name="a", shape=input_shape)],
compute_precision=ct.precision.FLOAT16,)
mlmodel.save("simple_test.mlpackage")
```


Environment:

- coremltools version: 7.0
- macOS Ventura 13.3.1 (MacBook Pro M1 Pro)

Start with the ct.convert() path for a MIL program, focusing on how inputs, TensorType, and EnumeratedShapes are handled for MLProgram conversion. Compare the generated MLPackage metadata with the supplied name and shapes; done means the renamed input and enumerated dimensions are visible in the converted model and Xcode Predictions tab.

Written by the indexing model from the issue text.