pytorch:モデル可視化,cpuとcudaのデバイス間におけるエラー

pytorchにおいてtorchsummaryでモデルの構造をファインチューニングするため、
print(summary(model, input_size=([(10,1684,40),(10)])))
を実行しました。forward関数は2入力となっているので、引数を２つ入力しています。モデルのforward関数における引数の次元数はそれぞれ３次元と１次元です。しかし、以下のようなエラーが発生しました。:

`TypeError: rand() argument after * must be an iterable, not int`

どうやら(10)が反復可能でないとのことなので、簡単に以下を試しました。
print(summary(model, input_size=([(10,1684,40),(10,20)])))
すると、以下のエラーが出ました。

`'lengths' argument should be a 1D CPU int64 tensor, but got 2D cuda:0 Long tensor`

二つ目の引数の(10)や(10,20)がlengthsに対応していることはわかりますが、もともとの次元数が１なのに１次元として入力すると上記のようにエラーになってしまうのです。なにか解決策はありますでしょうか。

torchsummary は最新版を使用しています。

ご助言いただいたことを元に修正を行いました。
print(summary(model, input_size=([(10,1684,40),(10,)])))
すると以下のエラーメッセージが出ました。

python
1---------------------------------------------------------------------------
2RuntimeError                              Traceback (most recent call last)
3~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in forward_pass(model, x, batch_dim, cache_forward_pass, device, **kwargs)
4    267             if isinstance(x, (list, tuple)):
5--> 268                 _ = model.to(device)(*x, **kwargs)
6    269             elif isinstance(x, dict):
7
8~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
9   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
10-> 1102             return forward_call(*input, **kwargs)
11   1103         # Do not call functions when jit is used
12
13~/06rnn_attentionf6/my_model.py in forward(self, input_sequence, input_lengths, label_sequence)
14     85         # エンコーダに入力する
15---> 86         enc_out, enc_lengths = self.encoder(input_sequence,
16     87                                             input_lengths)
17
18~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
19   1119 
20-> 1120         result = forward_call(*input, **kwargs)
21   1121         if _global_forward_hooks or self._forward_hooks:
22
23~/06rnn_attentionf6/encoder.py in forward(self, sequence, lengths)
24    101             rnn_input \
25--> 102                 = nn.utils.rnn.pack_padded_sequence(output, 
26    103                                                   output_lengths,
27
28~/.local/lib/python3.8/site-packages/torch/nn/utils/rnn.py in pack_padded_sequence(input, lengths, batch_first, enforce_sorted)
29    248     data, batch_sizes = \
30--> 249         _VF._pack_padded_sequence(input, lengths, batch_first)
31    250     return _packed_sequence_init(data, batch_sizes, sorted_indices, None)
32
33RuntimeError: 'lengths' argument should be a 1D CPU int64 tensor, but got 1D cuda:0 Long tensor
34
35The above exception was the direct cause of the following exception:
36
37RuntimeError                              Traceback (most recent call last)
38/tmp/ipykernel_704668/614744292.py in <module>
39      1 from torchinfo import summary
40----> 2 print(summary(model, input_size=([(10,1684,40),(10,)])))
41
42~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in summary(model, input_size, input_data, batch_dim, cache_forward_pass, col_names, col_width, depth, device, dtypes, row_settings, verbose, **kwargs)
43    199         input_data, input_size, batch_dim, device, dtypes
44    200     )
45--> 201     summary_list = forward_pass(
46    202         model, x, batch_dim, cache_forward_pass, device, **kwargs
47    203     )
48
49~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in forward_pass(model, x, batch_dim, cache_forward_pass, device, **kwargs)
50    275     except Exception as e:
51    276         executed_layers = [layer for layer in summary_list if layer.executed]
52--> 277         raise RuntimeError(
53    278             "Failed to run torchinfo. See above stack traces for more details. "
54    279             f"Executed layers up to: {executed_layers}"
55
56RuntimeError: Failed to run torchinfo. See above stack traces for more details. Executed layers up to:

また、この後、.cpu()により、CPUのマッチングを行い
print(summary(model, input_size=([(10,1684,40),(10,)])))
を再度実行しましたが、次は以下のエラーが生じました。

python
1---------------------------------------------------------------------------
2RuntimeError                              Traceback (most recent call last)
3~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in forward_pass(model, x, batch_dim, cache_forward_pass, device, **kwargs)
4    267             if isinstance(x, (list, tuple)):
5--> 268                 _ = model.to(device)(*x, **kwargs)
6    269             elif isinstance(x, dict):
7
8~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
9   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
10-> 1102             return forward_call(*input, **kwargs)
11   1103         # Do not call functions when jit is used
12
13~/06rnn_attentionf6/my_model.py in forward(self, input_sequence, input_lengths, label_sequence)
14     85         # エンコーダに入力する
15---> 86         enc_out, enc_lengths = self.encoder(input_sequence,
16     87                                             input_lengths)
17
18~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
19   1119 
20-> 1120         result = forward_call(*input, **kwargs)
21   1121         if _global_forward_hooks or self._forward_hooks:
22
23~/06rnn_attentionf6/encoder.py in forward(self, sequence, lengths)
24    101             rnn_input \
25--> 102                 = nn.utils.rnn.pack_padded_sequence(output, 
26    103                                                   output_lengths.cpu(), #ここを修正
27
28~/.local/lib/python3.8/site-packages/torch/nn/utils/rnn.py in pack_padded_sequence(input, lengths, batch_first, enforce_sorted)
29    248     data, batch_sizes = \
30--> 249         _VF._pack_padded_sequence(input, lengths, batch_first)
31    250     return _packed_sequence_init(data, batch_sizes, sorted_indices, None)
32
33RuntimeError: Length of all samples has to be greater than 0, but found an element in 'lengths' that is <= 0
34
35The above exception was the direct cause of the following exception:
36
37RuntimeError                              Traceback (most recent call last)
38/tmp/ipykernel_715630/614744292.py in <module>
39      1 from torchinfo import summary
40----> 2 print(summary(model, input_size=([(10,1684,40),(10,)])))
41
42~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in summary(model, input_size, input_data, batch_dim, cache_forward_pass, col_names, col_width, depth, device, dtypes, row_settings, verbose, **kwargs)
43    199         input_data, input_size, batch_dim, device, dtypes
44    200     )
45--> 201     summary_list = forward_pass(
46    202         model, x, batch_dim, cache_forward_pass, device, **kwargs
47    203     )
48
49~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in forward_pass(model, x, batch_dim, cache_forward_pass, device, **kwargs)
50    275     except Exception as e:
51    276         executed_layers = [layer for layer in summary_list if layer.executed]
52--> 277         raise RuntimeError(
53    278             "Failed to run torchinfo. See above stack traces for more details. "
54    279             f"Executed layers up to: {executed_layers}"
55
56RuntimeError: Failed to run torchinfo. See above stack traces for more details. Executed layers up to: []