This code shows how to initialize a conv in the __init__ method and use its parameters in the forward:
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        conv = nn.Conv2d(1, 6, 3)
        self.weight = conv.weight
        self.bias = conv.bias
        
    def forward(self, x):
        x = F.conv2d(x, self.weight, self.bias)
        return x

x = torch.randn(1, 1, 4, 4)
model = MyModel()
out = model(x)

You would have to add potential padding via F.pad, if it’s used in your conv layer.