def METHOD_NAME(pd, srcs, trg, transpose=True, bias=False):
    if len(srcs) == 1:
        for src in srcs:
            num = pd[src].detach().numpy()
            if bias:
                marianModel[trg] = np.atleast_2d(num).copy()
            else:
                if transpose:
                    marianModel[trg] = np.transpose(num).copy()
                else:
                    marianModel[trg] = num
    else: # path that joins matrices together for fused self-attention
        nums = [pd[src].detach().numpy() for src in srcs]
        if bias:
            nums = [np.transpose(np.atleast_2d(num)) for num in nums]
        marianModel[trg] = np.stack(nums, axis=0).copy()