Models on cuda are always moved to GPU 0 by torchinfo.summary

[t4rf9]

Describe the bug For CPU usage, torchinfo.summary works well. For models on GPU, if the model were not on GPU 0 (e.g. on GPU 5), torchinfo.summary will move it to GPU 0.

To Reproduce Steps to reproduce the behavior:

• execute the python script in command line to prevent python from terminating

  
  from torch import nn
  import torch
  from torchinfo import summary

linear = nn.Linear(1000, 1000).to(1) a = torch.randn(32, 1000).to(1) summary(linear, input_data=a)

- I encountered 2 cases: 
1. Run `nvidia-smi`, you will see on GPU 0 a python process
2. Replacing nn.Linear with torchaudio.transforms.MelSpectrogram or something similar, it will report that the model and the data are on different devices, which prevents the computation.

**Expected behavior**
summary should be executed on the given card

**Desktop (please complete the following information):**
 - OS: macOS 13.1, Ubuntu 16.04
 - torchinfo 1.7.1

[TylerYep]

Have you tried using the device parameter to summary?

[snimu]

I have sucessfully replicated the error.

Running

from torch import nn
import torch
from torchinfo import summary

linear = nn.Linear(1000, 1000).to(1)
a = torch.randn(32, 1000).to(1)
_ = summary(linear, input_data=a)
next(linear.parameters()).device

yields:

device(type='cuda', index=0)

And it turns out that using the device parameter works just fine:

from torch import nn
import torch
from torchinfo import summary

linear = nn.Linear(1000, 1000).to(1)
a = torch.randn(32, 1000).to(1)
_ = summary(linear, input_data=a, device=torch.device("cuda:1"))
next(linear.parameters()).device

yields:

device(type='cuda', index=1)

Still, this issue is adressed in pull-request#211.