Dynamic sequence length for transforme-based model - error when exporting from Python to MATLAB

We developed a simple transformer architecture (see the Python code below). This model, which we created using Python, can handle sequences of different lengths. I want to use my model in MATLAB. I tried to export the model to ONNX or to PT format. In both cases, I had to fix the input shape to export my model. I used the torch.jit.script() function in Python to trace and export my model in the .pt format. However, I think pytorchmex from the Deep Learning Toolbox Converter for PyTorch Models only works with torch.jit.trace.

I want to find a way to use a model in MATLAB that can accept inputs of any length.

Any help would be much appreciated.

# Python Code
# Model class to export
class TransformerModel(nn.Module):
    def __init__(
            self,
            input_dim,
            model_dim,
            n_classes,
            num_heads,
            num_layers,
        ):
        super(TransformerModel, self).__init__()
        self.model_dim = model_dim

        # Embedding Layer
        self.embedding = nn.Linear(input_dim, model_dim)


        # Transformer Encoder
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=model_dim,
            nhead=num_heads,
            batch_first=True
        )
        self.transformer_encoder = nn.TransformerEncoder(
            encoder_layer, 
            num_layers=num_layers
        )

        # Output Layer
        self.output_layer = nn.Linear(model_dim, n_classes)

    def forward(self, x, padding_mask):

        padding_mask = ~padding_mask

        x = self.embedding(x)
        # Transformer Encoder
        x = self.transformer_encoder(x, src_key_padding_mask=padding_mask)
        
        # Model prediction
        output = self.output_layer(x)

        return output