Thanks for the quick response!
x1 has values in [600, 800], x2 in [100, 200] and x3 in [10, 30]
So the model would become something like that if I didn’t wanna bother with padding:
class MyModel(nn.Module):
	def __init__(self):
		super(MyModel, self).__init__()
		self.cnn = models.resnet18(pretrained=True)
        self.cnn.fc = nn.Identity()
		self.fc = nn.Sequential(
			nn.Linear(self.cnn.fc.in_features, 256),
			nn.ReLU(),
			nn.Dropout(0.2),
			nn.Linear(256, 2),
			nn.LogSoftmax(dim=1))

	def forward(self, image, data):
		x1 = self.cnn.fc(image)
		x2 = data

		x = torch.cat((x1, x2), dim=1)
		x = self.fc(x)
		return x

model = MyModel()

Did I understand you correctly?