Simplified example show below:
import torch

def test_func(h: int, w: int) -> int:
    return h * w

class TestModule(torch.nn.Module):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        height, width = x.shape[2:]

        # Handle torch.fx
        if isinstance(x, torch.fx.proxy.Proxy):
            height, width = 224, 224
        
        
        hxw = test_func(height, width)
        
        return x * hxw


# Seems to work okay
torch.fx.symbolic_trace(TestModule())

Will this solution cause any issues? It seemed to work when I tested it, but I may be unaware of potential issues with this fix.
I want to deploy this solution in custom GoogleNet and Inception models, so I can use torchvision.models.feature_extraction.get_graph_node_names & torchvision.models.feature_extraction.create_feature_extractor on them.