TorchScript: Float(1, 32) vs. Tensor

I am trying to optimize a recurrent network using TorchScript. This is the code in question:

        s = self.f([obs[i], r, g, b], dim=-1))
        c = torch.sigmoid(self.phi_update([s, h], dim=-1)))
                                                    ~~~~~ <--- HERE

And the error is

Lists must contain only a single type, expected: Float(1, 32) but found Tensor instead

This is the definition of self.f:

        self.f = torch.jit.trace(
            nn.Linear(in_size, hidden_size), 
            example_inputs=torch.rand(1, in_size))

So Float(1, 32) appears to be the output of a traced module. Is there a way to coerce Float(1, 32) to Tensor or vice versa?

Thank you.