pytorch:netron及びtensorboardにおけるモデルのグラフ化のエラー

torchvizやtensorboard、netronによるモデルの可視化を行おうと思っています。
しかし、どのコマンドでも、以下のエラーが発生します。
以下はonnxからnetronに出力しようとした際のエラー

python
1input4 = torch.rand(10,1684,40)
2input4 = input4.to(device)
3input5 = torch.rand(10,)
4input5 = input5.cpu()
5#torch.set_default_tensor_type('torch.cuda.FloatTensor')
6onnx_path = "netForwatch.onnx"
7torch.onnx.export(model, (input4,input5), onnx_path)
8
9netron.start(onnx_path)

python
1
2---------------------------------------------------------------------------
3RuntimeError                              Traceback (most recent call last)
4/tmp/ipykernel_787804/3084293803.py in <module>
5      5 #torch.set_default_tensor_type('torch.cuda.FloatTensor')
6      6 onnx_path = "netForwatch.onnx"
7----> 7 torch.onnx.export(model, (input4,input5), onnx_path)
8      8 
9      9 netron.start(onnx_path)
10
11~/.local/lib/python3.8/site-packages/torch/onnx/__init__.py in export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, _retain_param_name, do_constant_folding, example_outputs, strip_doc_string, dynamic_axes, keep_initializers_as_inputs, custom_opsets, enable_onnx_checker, use_external_data_format)
12    314 
13    315     from torch.onnx import utils
14--> 316     return utils.export(model, args, f, export_params, verbose, training,
15    317                         input_names, output_names, operator_export_type, opset_version,
16    318                         _retain_param_name, do_constant_folding, example_outputs,
17
18~/.local/lib/python3.8/site-packages/torch/onnx/utils.py in export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, _retain_param_name, do_constant_folding, example_outputs, strip_doc_string, dynamic_axes, keep_initializers_as_inputs, custom_opsets, enable_onnx_checker, use_external_data_format)
19    105                       "Otherwise set to False because of size limits imposed by Protocol Buffers.")
20    106 
21--> 107     _export(model, args, f, export_params, verbose, training, input_names, output_names,
22    108             operator_export_type=operator_export_type, opset_version=opset_version,
23    109             do_constant_folding=do_constant_folding, example_outputs=example_outputs,
24
25~/.local/lib/python3.8/site-packages/torch/onnx/utils.py in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, example_outputs, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, fixed_batch_size, custom_opsets, add_node_names, use_external_data_format, onnx_shape_inference)
26    722 
27    723             graph, params_dict, torch_out = \
28--> 724                 _model_to_graph(model, args, verbose, input_names,
29    725                                 output_names, operator_export_type,
30    726                                 example_outputs, val_do_constant_folding,
31
32~/.local/lib/python3.8/site-packages/torch/onnx/utils.py in _model_to_graph(model, args, verbose, input_names, output_names, operator_export_type, example_outputs, do_constant_folding, _disable_torch_constant_prop, fixed_batch_size, training, dynamic_axes)
33    491         args = (args, )
34    492 
35--> 493     graph, params, torch_out, module = _create_jit_graph(model, args)
36    494 
37    495     params_dict = _get_named_param_dict(graph, params)
38
39~/.local/lib/python3.8/site-packages/torch/onnx/utils.py in _create_jit_graph(model, args)
40    435         return graph, params, torch_out, None
41    436     else:
42--> 437         graph, torch_out = _trace_and_get_graph_from_model(model, args)
43    438         state_dict = _unique_state_dict(model)
44    439         params = list(state_dict.values())
45
46~/.local/lib/python3.8/site-packages/torch/onnx/utils.py in _trace_and_get_graph_from_model(model, args)
47    386 
48    387     trace_graph, torch_out, inputs_states = \
49--> 388         torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True)
50    389     warn_on_static_input_change(inputs_states)
51    390 
52
53~/.local/lib/python3.8/site-packages/torch/jit/_trace.py in _get_trace_graph(f, args, kwargs, strict, _force_outplace, return_inputs, _return_inputs_states)
54   1164     if not isinstance(args, tuple):
55   1165         args = (args,)
56-> 1166     outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
57   1167     return outs
58
59~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
60   1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
61   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
62-> 1102             return forward_call(*input, **kwargs)
63   1103         # Do not call functions when jit is used
64   1104         full_backward_hooks, non_full_backward_hooks = [], []
65
66~/.local/lib/python3.8/site-packages/torch/jit/_trace.py in forward(self, *args)
67    125                 return tuple(out_vars)
68    126 
69--> 127         graph, out = torch._C._create_graph_by_tracing(
70    128             wrapper,
71    129             in_vars + module_state,
72
73RuntimeError: 0INTERNAL ASSERT FAILED at "../torch/csrc/jit/ir/alias_analysis.cpp":584, please report a bug to PyTorch. We don't have an op for aten::fill_ but it isn't a special case.  Argument types: Tensor, bool, 
74