Fix variable conversion code issue

        alpha = alpha * alpha * (3 - (2 * alpha))

        return theta0 + ((theta1 - theta0) * alpha)

    if os.path.exists(secondary_model_name):

        secondary_model_filename = secondary_model_name

        secondary_model_name = os.path.splitext(os.path.basename(secondary_model_name))[0]

    else:

        secondary_model_filename = 'models/' + secondary_model_name + '.ckpt'

    if os.path.exists(primary_model_name):

        primary_model_filename = primary_model_name

        primary_model_name = os.path.splitext(os.path.basename(primary_model_name))[0]

    else:

        primary_model_filename = 'models/' + primary_model_name + '.ckpt'

    print(f"Loading {secondary_model_filename}...")

    model_0 = torch.load(secondary_model_filename, map_location='cpu')

    if os.path.exists(secondary_model_name):

        secondary_model_filename = secondary_model_name

        secondary_model_name = os.path.splitext(os.path.basename(secondary_model_name))[0]

    else:

        secondary_model_filename = 'models/' + secondary_model_name + '.ckpt'

    print(f"Loading {primary_model_filename}...")

    model_1 = torch.load(primary_model_filename, map_location='cpu')

    primary_model = torch.load(primary_model_filename, map_location='cpu')

    print(f"Loading {secondary_model_filename}...")

    secondary_model = torch.load(secondary_model_filename, map_location='cpu')

    theta_0 = model_0['state_dict']

    theta_1 = model_1['state_dict']

    theta_0 = primary_model['state_dict']

    theta_1 = secondary_model['state_dict']

    theta_funcs = {

        "Weighted Sum": weighted_sum,

    print(f"Merging...")

    for key in tqdm.tqdm(theta_0.keys()):

        if 'model' in key and key in theta_1:

            theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)

            theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint

    for key in theta_1.keys():

        if 'model' in key and key not in theta_0:

    output_modelname = 'models/' + primary_model_name + '_' + str(interp_amount) + '-' + secondary_model_name + '_' + str(float(1.0) - interp_amount) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'

    print(f"Saving to {output_modelname}...")

    torch.save(model_0, output_modelname)

    torch.save(primary_model, output_modelname)

    print(f"Checkpoint saved.")

    return "Checkpoint saved to " + output_modelname