Exporting ConvTasNet to ONNX seems to be broken in >0.3.5

## 🐛 Bug

When I try to export a ConvTasNet model to ONNX, I get various error messages. See more info below.

### To Reproduce

```
import torch
from asteroid import ConvTasNet

model = ConvTasNet(n_src=1)

input_tensor = torch.zeros(1, 1, 12000, dtype=torch.float32)

model_output = model(input_tensor)

print(model_output.shape)

torch.onnx.export(
model, # model being run
input_tensor, # model input (or a tuple for multiple inputs)
'hello_convtasnet.onnx', # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=11, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names=["input"], # the model's input names
output_names=["output"], # the model's output names
)
```

### Expected behavior

I expect that the code runs without errors, and I expect to see a ~19 MB file called hello_convtasnet.onnx. I also want to be able to run the ONNX model with onnxruntime.

### Environment

I've tried to run the code above in various environments/configurations:

asteroid 0.3.4, pytorch 1.7.1, `opset_version=10` (I edited the value to this in the code), Windows

```
C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\asteroid\filterbanks\enc_dec.py:169: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if channels == 1 and self.as_conv1d:
Traceback (most recent call last):
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\symbolic_opset9.py", line 900, in _convert_padding_node
padding = [sym_help._get_const(v, 'i', 'padding') for v in input_list]
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\symbolic_opset9.py", line 900, in
padding = [sym_help._get_const(v, 'i', 'padding') for v in input_list]
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\symbolic_helper.py", line 105, in _get_const
raise RuntimeError("ONNX symbolic expected a constant value of the {} argument, got `{}`".format(arg_name, value))
RuntimeError: ONNX symbolic expected a constant value of the padding argument, got `3108 defined in (%3108 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3100, %3106) # C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\asteroid\utils\torch_utils.py:73:0
)`

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:/Users/iver5/Code/my_python_env/my_python_env/noise_reducer_pytorch/conv_tas_net_onnx_playground.py", line 20, in
output_names=["output"], # the model's output names
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\__init__.py", line 230, in export
custom_opsets, enable_onnx_checker, use_external_data_format)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 91, in export
use_external_data_format=use_external_data_format)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 639, in _export
dynamic_axes=dynamic_axes)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 421, in _model_to_graph
dynamic_axes=dynamic_axes, input_names=input_names)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 203, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\__init__.py", line 263, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 934, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\symbolic_opset9.py", line 912, in constant_pad_nd
padding = _convert_padding_node(padding)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\symbolic_opset9.py", line 902, in _convert_padding_node
return sym_help._onnx_opset_unsupported_detailed('Pad', 9, 11, 'The sizes of the padding must be constant')
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\symbolic_helper.py", line 196, in _onnx_opset_unsupported_detailed
'opset {}. {}. Please try opset version {}.'.format(op_name, current_opset, reason, supported_opset))
RuntimeError: Unsupported: ONNX export of Pad in opset 9. The sizes of the padding must be constant. Please try opset version 11.

Process finished with exit code 1
```

---

asteroid 0.3.5, pytorch 1.7.1, ONNX opset version 11, Windows

*It executes/exports without errors*

---

asteroid 0.3.5, pytorch 1.8.0, ONNX opset version 11, Windows

*It executes/exports without errors*

---

asteroid 0.4.5, pytorch 1.7.1, ONNX opset version 11, Windows

```
C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\asteroid\masknn\convolutional.py:206: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
skip_connection = torch.tensor([0.0], device=output.device)
Traceback (most recent call last):
File "C:/Users/iver5/Code/my_python_env/my_python_env/noise_reducer_pytorch/conv_tas_net_onnx_playground.py", line 20, in
output_names=["output"], # the model's output names
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\__init__.py", line 230, in export
custom_opsets, enable_onnx_checker, use_external_data_format)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 91, in export
use_external_data_format=use_external_data_format)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 639, in _export
dynamic_axes=dynamic_axes)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 421, in _model_to_graph
dynamic_axes=dynamic_axes, input_names=input_names)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 203, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\__init__.py", line 263, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\utils.py", line 934, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "C:\Users\iver5\Anaconda3\envs\my_python_env-gpu\lib\site-packages\torch\onnx\symbolic_opset9.py", line 1567, in tensor
dtype = data.type().scalarType()
RuntimeError: r INTERNAL ASSERT FAILED at "..\\aten\\src\\ATen/core/jit_type.h":171, please report a bug to PyTorch.
```

---

asteroid 0.4.0rc0, pytorch 1.8.0, ONNX opset version 11, Windows

```
C:\Users\iver5\Anaconda3\lib\site-packages\asteroid\masknn\convolutional.py:187: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
skip_connection = torch.tensor([0.0], device=output.device)
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Traceback (most recent call last):
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\iver5\Code\my_python_env\my_python_env\noise_reducer_pytorch\conv_tas_net_onnx_playground.py", line 12, in
torch.onnx.export(
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 271, in export
return utils.export(model, args, f, export_params, verbose, training,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 88, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 691, in _export
_model_to_graph(model, args, verbose, input_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 460, in _model_to_graph
graph = _optimize_graph(graph, operator_export_type,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 206, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 309, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 994, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_opset9.py", line 344, in wrapper
return overload(g, *args)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 142, in wrapper
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 142, in
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 93, in _parse_arg
raise RuntimeError("Expected node type 'onnx::Constant' "
RuntimeError: Expected node type 'onnx::Constant' for argument 'dim' of node 'reduce_dim', got 'onnx::Range'.
```

---

asteroid 0.4.5, pytorch 1.8.0, ONNX opset version 11, Windows

```
C:\Users\iver5\Anaconda3\lib\site-packages\asteroid\masknn\convolutional.py:187: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
skip_connection = torch.tensor([0.0], device=output.device)
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Traceback (most recent call last):
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\iver5\Code\my_python_env\my_python_env\noise_reducer_pytorch\conv_tas_net_onnx_playground.py", line 12, in
torch.onnx.export(
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 271, in export
return utils.export(model, args, f, export_params, verbose, training,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 88, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 691, in _export
_model_to_graph(model, args, verbose, input_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 460, in _model_to_graph
graph = _optimize_graph(graph, operator_export_type,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 206, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 309, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 994, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_opset9.py", line 344, in wrapper
return overload(g, *args)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 142, in wrapper
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 142, in
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 93, in _parse_arg
raise RuntimeError("Expected node type 'onnx::Constant' "
RuntimeError: Expected node type 'onnx::Constant' for argument 'dim' of node 'reduce_dim', got 'onnx::Range'.
```

---

asteroid 0.4.5, pytorch 1.8.0, ONNX opset version 11, torchaudio==0.8.0, Windows

```
C:\Users\iver5\Anaconda3\lib\site-packages\asteroid\masknn\convolutional.py:187: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
skip_connection = torch.tensor([0.0], device=output.device)
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Warning: ONNX Preprocess - Removing mutation on block inputs. This changes graph semantics.
Traceback (most recent call last):
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\iver5\Code\my_python_env\my_python_env\noise_reducer_pytorch\conv_tas_net_onnx_playground.py", line 12, in
torch.onnx.export(
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 271, in export
return utils.export(model, args, f, export_params, verbose, training,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 88, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 691, in _export
_model_to_graph(model, args, verbose, input_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 460, in _model_to_graph
graph = _optimize_graph(graph, operator_export_type,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 206, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 309, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 994, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_opset9.py", line 344, in wrapper
return overload(g, *args)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 142, in wrapper
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 142, in
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 93, in _parse_arg
raise RuntimeError("Expected node type 'onnx::Constant' "
RuntimeError: Expected node type 'onnx::Constant' for argument 'dim' of node 'reduce_dim', got 'onnx::Range'.
```

---

asteroid 0.4.5, pytorch 1.9.0, ONNX opset version 11, torchaudio==0.9.0, Windows

```
C:\Users\iver5\Anaconda3\lib\site-packages\asteroid\masknn\convolutional.py:206: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
skip_connection = torch.tensor([0.0], device=output.device)
Traceback (most recent call last):
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\iver5\Code\my_python_env\my_python_env\noise_reducer_pytorch\conv_tas_net_onnx_playground.py", line 12, in
torch.onnx.export(
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 275, in export
return utils.export(model, args, f, export_params, verbose, training,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 88, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 689, in _export
_model_to_graph(model, args, verbose, input_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 463, in _model_to_graph
graph = _optimize_graph(graph, operator_export_type,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 200, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 313, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 994, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_opset9.py", line 389, in wrapper
return overload(g, *args)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 166, in wrapper
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore[assignment]
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 166, in
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore[assignment]
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 93, in _parse_arg
raise RuntimeError("Expected node type 'onnx::Constant' "
RuntimeError: Expected node type 'onnx::Constant' for argument 'dim' of node 'reduce_dim', got 'onnx::Range'.
```

---

asteroid 0.5.1, pytorch 1.9.0, ONNX opset version 11, torchaudio==0.9.0, Windows

```
C:\Users\iver5\Anaconda3\lib\site-packages\asteroid\masknn\convolutional.py:205: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
skip_connection = torch.tensor([0.0], device=output.device)
Traceback (most recent call last):
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\iver5\Anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\iver5\Code\my_python_env\my_python_env\noise_reducer_pytorch\conv_tas_net_onnx_playground.py", line 12, in
torch.onnx.export(
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 275, in export
return utils.export(model, args, f, export_params, verbose, training,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 88, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 689, in _export
_model_to_graph(model, args, verbose, input_names,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 463, in _model_to_graph
graph = _optimize_graph(graph, operator_export_type,
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 200, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\__init__.py", line 313, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 994, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_opset9.py", line 389, in wrapper
return overload(g, *args)
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 166, in wrapper
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore[assignment]
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 166, in
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore[assignment]
File "C:\Users\iver5\Anaconda3\lib\site-packages\torch\onnx\symbolic_helper.py", line 93, in _parse_arg
raise RuntimeError("Expected node type 'onnx::Constant' "
RuntimeError: Expected node type 'onnx::Constant' for argument 'dim' of node 'reduce_dim', got 'onnx::Range'.
```

Start by reproducing the reported ConvTasNet export failures across the listed Asteroid and PyTorch versions, then inspect asteroid/filterbanks/enc_dec.py, asteroid/utils/torch_utils.py, and asteroid/masknn/convolutional.py at the traced operations. Compare the generated ONNX model and onnxruntime behavior with the successful 0.3.5 case; done means a supported configuration exports without errors and runs in onnxruntime.

Written by the indexing model from the issue text.