The error message sounds like you already changed the conv_class layer.
Could you check, if you are using two different layers as the output, since the error points to conv_class, while you are manipulating conv_classify.
Here is a small code snippet of what I was thinking about:
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 50, 3, 1, 1)
        self.conv_classify = nn.Conv2d(50, 2, 1, 1)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.conv_classify(x)
        return x
    
# Init model and train
model = MyModel()    
x = torch.randn(1, 1, 4, 4)    
output = model(x)    
print(output.shape)

# Save "trained" state_dict
torch.save(model.state_dict(), 'mymodel.pth')

# Reload model and load state_dict
model = MyModel()
model.load_state_dict(torch.load('mymodel.pth'))

# Change last conv layer
model.conv_classify = nn.Conv2d(50, 4, 1, 1)
output = model(x)
print(output.shape)