PyTorch __ior__ op is not implemented for conversion

**Description**

Hello,

I encountered an issue while trying to convert a PyTorch model (gemma-3-1b-it) to Core ML format. The conversion process failed with the following error: PyTorch convert function for op '__ior__' not implemented..

I understand there was a recent fix related to an __ior__ issue in RangeDim. I have confirmed that I am using the latest version of coremltools by installing directly from the main branch of this repository.

**Steps to Reproduce**

Install coremltools from the main branch: `pip install git+https://github.com/apple/coremltools.git`

Run the provided Python script with the gemma-3-1b-it model.

The conversion fails with the traceback shown below.

**Environment**

CoreMLTools Version: `coremltools @ git+https://github.com/apple/coremltools.git@0f4244215c1f293f9b822b194fede05ad0e93851`

PyTorch Version: `2.2.2`

Python Version: `3.12.10`

Additional Information

**Here is the traceback I received:**
```
Failed to load _MLModelProxy: No module named 'coremltools.libcoremlpython'
...
(Omitted - 'coremltools.libcoremlpython' related errors)
...
Fail to import BlobReader from libmilstoragepython. No module named 'coremltools.libmilstoragepython'
...
(Omitted - 'coremltools.libmilstoragepython' related errors)
...
Failed to load '_MLCPUComputeDeviceRemoteProxy'. Remote device functionality for retrieving the compute plan is unavailable.
...
(Omitted - 'RemoteProxy' related errors)
...
CoreMLTools Version: 9.0b1
PyTorch Version: 2.2.2
Numpy Version: 1.26.4
Loading Hugging Face model from '.../gemma-3-1b-it' into memory...
Model loaded and configured.
Wrapper model prepared.
Tracing wrapper model to TorchScript...
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
/Users/.../transformers/masking_utils.py:190: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect.
...
(Omitted - TracerWarning)
...
TorchScript tracing complete.
Converting TorchScript model to Core ML...
Model is not in eval mode. Consider calling '.eval()' on your model prior to conversion
Converting PyTorch Frontend ==> MIL Ops: 0%| | 0/4901 [00:00 MIL Ops: 0%| | 23/4901 [00:00

ERROR - converting '__ior__' op (located at: 'model/model/attention_mask.19'):

Converting PyTorch Frontend ==> MIL Ops: 1%| | 57/4901 [00:00
Conversion to CoreML failed: PyTorch convert function for op '__ior__' not implemented.
```
**Here is my `convert.py` script:**
```python
import coremltools as ct
import numpy as np
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForCausalLM
import argparse
import os

def main():
# Configuration for parsing command line arguments
parser = argparse.ArgumentParser(description="Convert a Hugging Face model to Core ML.")
parser.add_argument(
"--model",
type=str,
required=True,
help="Path to the downloaded Hugging Face model directory (e.g., 'gemma-3-1b-it')."
)
args = parser.parse_args()

# Use the path received as a command line argument
downloaded_hf_model_dir = args.model

print(f"CoreMLTools Version: {ct.__version__}")
print(f"PyTorch Version: {torch.__version__}")
print(f"Numpy Version: {np.__version__}")

try:
# 1. Hugging Face model loading
print(f"Loading Hugging Face model from '{downloaded_hf_model_dir}' into memory...")
model = AutoModelForCausalLM.from_pretrained(downloaded_hf_model_dir, torch_dtype=torch.float16)
model.eval()
model.config.use_cache = False
print("Model loaded and configured.")

# 2. Create a wrapper model for Core ML conversion
class GemmaCoreMLWrapper(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
self.model.config.use_cache = False

def forward(self, input_ids, attention_mask):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
use_cache=False,
return_dict=False,
output_attentions=False,
output_hidden_states=False
)
logits = outputs[0]
return logits

wrapped_model = GemmaCoreMLWrapper(model)
print("Wrapper model prepared.")

# 3. Prepare dummy inputs for TorchScript tracing
max_seq_length = 1024
tokenizer = AutoTokenizer.from_pretrained(downloaded_hf_model_dir)

dummy_input_ids = torch.randint(0, tokenizer.vocab_size, (1, 10), dtype=torch.long)
dummy_attention_mask = torch.ones(1, 10, dtype=torch.long)

# 4. Trace the wrapper model to TorchScript
print("Tracing wrapper model to TorchScript...")
traced_model = torch.jit.trace(wrapped_model, (dummy_input_ids, dummy_attention_mask))
print("TorchScript tracing complete.")

# 5. Convert TorchScript model to Core ML
print("Converting TorchScript model to Core ML...")
coreml_model = ct.convert(
traced_model,
inputs=[
ct.TensorType(name="input_ids", shape=(1, ct.RangeDim(upper_bound=max_seq_length)), dtype=np.int32),
ct.TensorType(name="attention_mask", shape=(1, ct.RangeDim(upper_bound=max_seq_length)), dtype=np.int32)
],
source="pytorch",
convert_to="mlprogram",
minimum_deployment_target=ct.target.iOS16
)

model_name = os.path.basename(downloaded_hf_model_dir)
output_filename = f"{model_name}-coreml.mlpackage"
coreml_model.save(output_filename)
print(f"CoreML model saved successfully to {output_filename}.")

except Exception as e:
print(f"Conversion to CoreML failed: {e}")

if __name__ == "__main__":
main()

```
**How to Use convert.py**

This script is designed to be run from the command line. You need to provide the path to the model you want to convert using the --model argument.
**Basic Command**
`python convert.py --model "[path_to_your_model]"`

**Example**

If your model is located at `/path/your/directory/gemma-3-1b-it`, the command would be:
`python convert.py --model "/path/your/directory/gemma-3-1b-it"`

**important Notes**

**Current Directory**: Make sure you are in the project's root directory when you run the command.

Start with the supplied convert.py script and reproduce the failure at ct.convert, focusing on the PyTorch frontend error for the '__ior__' operation. Trace how the attention_mask operation reaches conversion and determine the expected Core ML representation. Done means the provided Gemma conversion no longer stops at an unimplemented '__ior__' error, with a regression check if the repository has a suitable test location.

Written by the indexing model from the issue text.