def METHOD_NAME(params, info, func):
    if 'Affine' in info._convert_context:
        transposed = info._convert_context['Affine']
    else:
        transposed = set()
    for idx in params:
        weight_name = func.input[idx]
        if weight_name in transposed:
            return
        w_shape = info._variables[weight_name].shape.dim[:]
        if weight_name in info._parameters:
            w_data = info._parameters[weight_name]
            transposed.add(weight_name)
            info._convert_context['Affine'] = transposed
        else:
            print(
                "WARNING: affine weight is not transposed. Since it is not included in .nntxt/.nnp")
        i_num = w_shape[0]
        data = np.array(w_data.data[:])
        data = data.reshape(int(i_num), -1)
        data = np.transpose(data)
        del info._parameters[weight_name].data[:]
        info._parameters[weight_name].data.extend(data.flatten())