Graph is renamed in torch.jit
Labels: oncall: jit

### 🐛 Describe the bug

The graph of a model is renamed when is converted into torchscript.

I have prepared a toy model for this.

```
import torch
import torchvision
import tensorrt
import torch_tensorrt

class ToyModel(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.gaus = torchvision.transforms.GaussianBlur([33, 33], [4., 4.])
        self.conv = torch.nn.Conv2d(3, 64, (3,3))
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.relu(self.conv(x))
        x = self.gaus(x)
        return x

model1 = ToyModel().eval().to('cuda')
model2 = ToyModel().eval().to('cuda')

traced1 = torch.jit.trace(model1, torch.randn(1,3,224,224).to('cuda'))
traced2 = torch.jit.trace(model2, torch.randn(1,3,224,224).to('cuda'))

print(traced1.graph)
print(traced2.graph)
```

The names in the graphs change as I show below
Here the first graph
```
graph(%self.1 : __torch__.ToyModel,
      %x.1 : Float(1, 3, 224, 224, strides=[150528, 50176, 224, 1], requires_grad=0, device=cuda:0)):
  %gaus : __torch__.torchvision.transforms.transforms.GaussianBlur = prim::GetAttr[name="gaus"](%self.1)
  %relu : __torch__.torch.nn.modules.activation.ReLU = prim::GetAttr[name="relu"](%self.1)
  %conv : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="conv"](%self.1)
  %190 : Tensor = prim::CallMethod[name="forward"](%conv, %x.1)
  %191 : Tensor = prim::CallMethod[name="forward"](%relu, %190)
  %192 : Tensor = prim::CallMethod[name="forward"](%gaus, %191)
  return (%192)
```

Here the second graph
```
graph(%self.1 : __torch__.___torch_mangle_7.ToyModel,
      %x.1 : Float(1, 3, 224, 224, strides=[150528, 50176, 224, 1], requires_grad=0, device=cuda:0)):
  %gaus : __torch__.torchvision.transforms.transforms.___torch_mangle_4.GaussianBlur = prim::GetAttr[name="gaus"](%self.1)
  %relu : __torch__.torch.nn.modules.activation.___torch_mangle_6.ReLU = prim::GetAttr[name="relu"](%self.1)
  %conv : __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d = prim::GetAttr[name="conv"](%self.1)
  %190 : Tensor = prim::CallMethod[name="forward"](%conv, %x.1)
  %191 : Tensor = prim::CallMethod[name="forward"](%relu, %190)
  %192 : Tensor = prim::CallMethod[name="forward"](%gaus, %191)
  return (%192)
```

Why happens this rename? This is relationed with other error https://github.com/pytorch/TensorRT/issues/1526

### Versions

PyTorch version: 1.11.0+cu113
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.6 (main, Nov  2 2022, 18:53:38) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: 
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090
Nvidia driver version: 525.60.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==1.11.0+cu113
[pip3] torch-tensorrt==1.1.0
[pip3] torchvision==0.12.0+cu113
[conda] No relevant packages


cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel