Torch.jit.script cannot index a ModuleList with an int returned from range()

Hello everyone,
I’m trying to get a script of a model.
Here is the code(I removed most of the code in init function to avoid verbosity):
(spec.matrix is a square matrix that its shape is not predetermined.)

class Cell(nn.Module):

    def __init__(self, spec, in_channels, out_channels):
        super(Cell, self).__init__()
        self.spec = spec
        self.num_vertices = self.spec.matrix.shape[0]

        # vertex_channels[i] = number of output channels of vertex i
        self.vertex_channels = ComputeVertexChannels(in_channels, out_channels, self.spec.matrix)

    def forward(self, x: Any) -> Any:
        tensors = [x]

        out_concat = []

        for t in range(1, self.num_vertices - 1):

            fan_in = []
            for  src in range(1, t):
                if self.spec.matrix[src, t]:
                    fan_in.append(Truncate(tensors[src], torch.tensor(self.vertex_channels[t])))

            if self.spec.matrix[0, t]:

            # perform operation on node

            vertex_input = sum(fan_in)
            vertex_output = self.vertex_op[t](vertex_input)

            if self.spec.matrix[t, self.num_vertices - 1]:

        if not out_concat:
            assert self.spec.matrix[0, self.num_vertices - 1]
            outputs = self.input_op[self.num_vertices - 1](tensors[0])
            if len(out_concat) == 1:
                outputs = out_concat[0]
                outputs =, 1)

            if self.spec.matrix[0, self.num_vertices - 1]:
                outputs += self.input_op[self.num_vertices - 1](tensors[0])

        return outputs

def Truncate(inputs, channels):
    """Slice the inputs to channels if necessary."""
    input_channels = inputs.size()[1]
    if input_channels < channels:
        raise ValueError('input channel < output channels for truncate')
    elif input_channels == channels:
        return inputs  # No truncation necessary
        # Truncation should only be necessary when channel division leads to
        # vertices with +1 channels. The input vertex should always be projected to
        # the minimum channel count.
        assert input_channels - channels == 1
        return inputs[:, :channels, :, :]

It returns the following RuntimeError:

Truncate(Tensor inputs, Tensor channels) -> (Tensor):
Expected a value of type 'Tensor (inferred)' for argument 'inputs' but instead found type 'Any'.
Inferred 'inputs' to be of type 'Tensor' because it was not annotated with an explicit type.:
  File "", line 136
            for  src in range(1, t):
                if self.spec.matrix[src, t]:
                    fan_in.append(Truncate(tensors[src], torch.tensor(self.vertex_channels[t])))
                                  ~~~~~~~~ <--- HERE
            if self.spec.matrix[0, t]:

As error declares, I should annotate the input in tensors. However, doing that will result in RuntimeError:Expected integer literal for index ....
As I understood from this post I tried adding @ torch.jit.interface before the class Cell(nn.Module). It returned RuntimeError:interface declarations must have a return type annotated. and it underlines all the lines in the init function.
Although I can shun the problem by adding a @torch.jit.ignore before the forward function in the Cell Module, it seems a bit of an unreliable solution.
Thank you in advance for any guidance.