Conflict with torch.distributed?

Hello, I encounted an error when calling `flop_count_table()` in my distributed training code.
The error message is as below. But I checked the input of function `allgather()` and didn't find anything unusual.

> File "/xxx/anaconda3/envs/torch13/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 2275, in all_gather
> work = default_pg.allgather([tensor_list], [tensor])
>RuntimeError: unsupported input list type: Tensor[]
>ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 628698) of binary: /xxx/anaconda3/envs/torch13/bin/python

Here's a brief code which can regenerate my error by calling `python -m torch.distributed.run --nproc_per_node=1 --master_port 10603 try.py`
```
import torch
import torch.nn as nn

from fvcore.nn import FlopCountAnalysis, flop_count_str, flop_count_table

class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
self.fc = nn.Linear(10, 1)

def forward(self, x):
y = self.fc(x)
concat_all_gather(y)
return y.sum()

@torch.no_grad()
def concat_all_gather(tensor):
tensors_gather = [torch.ones_like(tensor)
for _ in range(torch.distributed.get_world_size())]
torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
output = torch.cat(tensors_gather, dim=0)
return output

torch.distributed.init_process_group(backend='nccl')
local_rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()

model = SimpleModel().cuda()
model = nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank)

flop = FlopCountAnalysis(model.module, torch.randn(100, 10).cuda())
print(flop_count_table(flop, max_depth=7, show_param_shapes=True))

torch.distributed.destroy_process_group()
```
Additionally, my environment is:
`Python 3.9.18`, `cuda-11.7`, `fvcore==0.1.5.post20221221`, `torch 1.13`

Another confusing thing is, in the `python3.8.18` & `cuda-11.4` & `torch 1.10` environment, the above doesn't result in an error.