Unrecognized function or variable 'newShape' in auto-generated function pyUnsqueeze.m

I'm trying to import a custom pytorch model in matlab using "importNetworkFromPyTorch", but I'm having some issues with the matlab autonomously generated function "pyUnsqueeze"

My model receives as imput an array with shape [1 10 8] and ouputs one shaped [1,10,1].

I import my net as

net = importNetworkFromPyTorch("path_to_my_model", 'PyTorchInputSizes', [1,10,8])

then to initialze it I run as prompted by the warning on the command window:

dlX1 = dlarray(rand([1,10,8]), 'UUU');

and then

net = initialize(net, dlX1);

upon which I get the error:

Error using dlnetwork/initialize (line 600)
Invalid network.

Caused by:
    Layer 'TopLevelModule': Invalid network.
        Layer 'ATEN3': Error using the predict function in layer empty_model_traced.TopLevelModule_ATEN3. The function threw an
        error and could not be executed.
            Unrecognized function or variable 'newShape'.

            Error in empty_model_traced.ops.pyUnsqueeze (line 41)
            Yval = reshape(Xval, newShape);
                                 ^^^^^^^^
            Error in empty_model_traced.TopLevelModule_ATEN3/tracedPyTorchFunction (line 91)
                        [unsqueeze_input_1] = empty_model_traced.ops.pyUnsqueeze(arange_38, Constant_30);
                                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
            Error in empty_model_traced.TopLevelModule_ATEN3/predict (line 46)
                        [unsqueeze_input_1] = tracedPyTorchFunction(obj,size_batchu_1,false,"predict");
                                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The pyUnsqueeze.m function is:

function Y = pyUnsqueeze(X, dim)
%PYUNSQUEEZE Inserts a singleton dimension at the position given by dim.
% at::Tensor at::unsqueeze(const at::Tensor &self, int64_t dim)

%   Copyright 2022-2023 The MathWorks, Inc.

import empty_model_traced.ops.*

dim = dim.value;

Xval = X.value;
Xrank = X.rank;

% Convert dim to reverse-pytorch
if (dim<0)
    dim = -dim;
else
    dim = Xrank - dim + 1; 
end

% Reshape the data, inserting a singleton dim
Yrank = Xrank + 1;
if Yrank == 1
    newShape = size(Xval);
elseif Yrank == 2
    if dim==0
        % X is a 1D vector, which will be a col in MATLAB ([N 1]).
        % The new singleton dim should be inserted at the front if dim=0
        newShape = flip(size(Xval));
    elseif dim==1
        % The new singleton dim should be inserted at the end if dim=1.
        % X is already [N 1], so no need to flip it.
        newShape = size(Xval);
    end
else
    newShape = ones(1, Yrank);
    knownSizes = setdiff(1:Yrank, dim);
    newShape(knownSizes) = size(Xval, 1:numel(knownSizes));
end

Yval = reshape(Xval, newShape);
Yval = dlarray(Yval, repmat('U', 1, max(2,Yrank)));
Y = struct('value', Yval, 'rank', Yrank);
end

With some debugging i get that Yrank = 2, and dim = 2, for which there is no case in the if-else list, hence newshape is never defined.

I can track back this to the python code:

pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)  # shape (1, t)

Now, is this the correct behavior of the matlab function, and I'm doing something wrong in my code or is this a bug in the auto-generated function?

[Alessandro]

It looks like the issue is with the "convert dim to reverese-pythorch" if-else block

instead of

if (dim<0)
    dim = -dim;
else
    dim = Xrank - dim + 1; 
end

it should be

if (dim<0)
    dim = Xrank + dim + 1; 
end

to correctly implement the negative python index, as highlighted in the torch.unsqueeze docs