ValueError: No matching select or slice.

```
import torch
import torch.nn as nn
import numpy as np
import coremltools as ct
import librosa
from fairseq import checkpoint_utils

audio, _ = librosa.load("/Users/admin/Desktop/yao/VC/data/test_women/women.mp3", sr=16000)
feats = torch.from_numpy(audio)
feats = feats.float()
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
in_feats = feats.view(1, -1)

class CustomModule(nn.Module):
def __init__(self):
super(CustomModule, self).__init__()

def forward(self, in_feats):
device = 'cpu'
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(["/Users/admin/Desktop/yao/VC/weights/hubert_base.pt"], suffix="")
hubert_model = models[0]
hubert_model = hubert_model.to(device)
hubert_model = hubert_model.float()
hubert_model.eval()
pm = np.zeros(in_feats.shape, dtype=bool)

in_feats.to(device)
padding_mask = torch.BoolTensor(pm).to(device)
output_layer = 12

with torch.no_grad():
feats = hubert_model.extract_features(source=in_feats, padding_mask=padding_mask, output_layer=output_layer)[0]

return feats

def export_hubert():
hubert = CustomModule()
hubert.eval()
traced_model = torch.jit.trace(hubert, in_feats)

mlmodel = ct.convert(
traced_model,
source='pytorch',
inputs=[ct.TensorType(name="audio", shape=audio.shape, dtype=np.float32)],
compute_units=ct.ComputeUnit.CPU_AND_GPU,
minimum_deployment_target=ct.target.macOS13)

mlmodel.save("tools/hubert.mlmodel")

if __name__ == "__main__":
export_hubert()
```

error like this:
```
Traceback (most recent call last):
File "/Users/admin/Desktop/yao/VC/tools/export_coreml.py", line 144, in
export_hubert()
File "/Users/admin/Desktop/yao/VC/tools/export_coreml.py", line 132, in export_hubert
mlmodel = ct.convert(
File "/Users/admin/opt/anaconda3/lib/python3.9/site-packages/coremltools/converters/_converters_entry.py", line 574, in convert
mlmodel = mil_convert(
File "/Users/admin/opt/anaconda3/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 188, in mil_convert
return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
File "/Users/admin/opt/anaconda3/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 212, in _mil_convert
proto, mil_program = mil_convert_to_proto(
File "/Users/admin/opt/anaconda3/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 286, in mil_convert_to_proto
prog = frontend_converter(model, **kwargs)
File "/Users/admin/opt/anaconda3/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 108, in __call__
return load(*args, **kwargs)
File "/Users/admin/opt/anaconda3/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 71, in load
converter = TorchConverter(
File "/Users/admin/opt/anaconda3/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/converter.py", line 363, in __init__
p(self.graph)
File "/Users/admin/opt/anaconda3/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/torchir_passes.py", line 151, in generate_tensor_assignment_ops
raise ValueError("No matching select or slice.")
ValueError: No matching select or slice.
```

Reproduce the failure from tools/export_coreml.py, starting at the ct.convert call and the traced CustomModule. Read the traceback through coremltools/converters/mil/frontend/torch/torchir_passes.py, then determine which generated graph assignment causes the missing select or slice match. Done means the reported HubERT conversion completes without this ValueError and the resulting model is saved as shown.

Written by the indexing model from the issue text.