CoreML segfaults on torch.nn.Conv1d

## 🐞Describing the bug
CoreML segfaults when running torch.ops.aten.conv1d.default.

## To Reproduce
```

import torch

class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv1d(16, 4, 6, stride=8, padding=0, dilation=2, groups=2, bias=False)
def forward(self, x):
return self.conv(x)

model = Model()
inputs = (
torch.randn(2, 16, 11),
)

eager_outputs = model(*inputs)

ep = torch.export.export(model, inputs)

import coremltools as ct
import numpy as np
ep = ep.run_decompositions({})

eager_outputs = model(*inputs)

mlmodel = ct.convert(ep)

coreml_inputs = mlmodel.get_spec().description.input
coreml_outputs = mlmodel.get_spec().description.output
predict_inputs = {str(ct_in.name): pt_in.detach().cpu().numpy().astype(np.int32) for ct_in, pt_in in zip(coreml_inputs, inputs)}
out = mlmodel.predict(predict_inputs)

print("Eager", eager_outputs)
print("CoremL", out)
```

This above code results in a segfault.

## System environment (please complete the following information):
- coremltools version: 8.3
- OS (e.g. MacOS version or Linux type): macOS15

Start by running the provided Python reproduction through torch.ops.aten.conv1d.default, torch.export.export, and ct.convert on macOS 15 with coremltools 8.3. Trace the failure around CoreML conversion and prediction; done means the Conv1d model converts and predicts without a segmentation fault.

Written by the indexing model from the issue text.