Hi,

I’ve been trying to convert torch model to TensorRT model using `torch_tensorrt`

tool. The following code is used to convert model to TensorRT model:

```
trt_model = torch_tensorrt.compile(torch_model,
inputs = [
torch_tensorrt.Input((2048, 1, 32, 32),
dtype=torch.float32)
],
enabled_precisions = precision,
workspace_size = 1 << 33
)
```

However, in `forward`

function of the model uses a global variable and it is causing problem in TensorRT conversion. Here is the error prompt:

```
python value of type 'float' cannot be used as a value. Perhaps it is a closed over global variable? If so, please consider passing it in as an argument or use a local varible instead.:
File "/codebase/Net/model.py", line 65
def forward(self, patch):
descr = self.desc_norm(self.layers(patch) + eps_l2_norm)
~~~~~~~~~~~ <--- HERE
descr = descr.view(descr.size(0), -1)
return descr
```

My initial solution is to add `eps_12_norm`

as class attribute in network class. I tried creating a wrapper `torch.nn.module`

class which loads weights from original model and included `eps_12_norm`

new attribute and saved it as a new model but error still remains the same in TensorRT model conversion. I tried loading the original weights into original class which has new class attribute `eps_12_norm`

, I cannot load the pretrained weights either.

My other solution is that I have to copy the weights from old model to new model with new class attribute since the only change here is new attribute addition. The model has two `torch.nn.sequential`

module, I used the following code to copy the weights from old model to new model:

```
# copy weights from old model layers module to new model layers module
source_layers = old_model.layers
target_layers = new_model.layers
for source_param, target_param in zip(source_layers.parameters(), target_layers.parameters()):
target_param.data.copy_(source_param.data)
# copy weights from old model desc_norm module to new model desc_norm module
source_desc_norm = old_model.desc_norm
target_desc_norm = new_model.desc_norm
for source_param, target_param in zip(source_desc_norm.parameters(), target_desc_norm.parameters()):
target_param.data.copy_(source_param.data)
```

However, the inference results from two models over same input data is not the same. How can I solve this situation?