def METHOD_NAME(group, values, graph):
    """Compute scores by scales of batch_norm layer.
    """
    assert (isinstance(graph, GraphWrapper))
    # step1: Get first convolution
    conv_weight = group.master["name"]
    axis = group.master["axis"]
    value = values[conv_weight]
    param_var = graph.var(conv_weight)
    conv_op = param_var.outputs()[0]
    # step2: Get bn layer after first convolution
    conv_output = conv_op.outputs("Output")[0]
    bn_op = conv_output.outputs()[0]
    if bn_op is not None:
        bn_scale_param = bn_op.inputs("Scale")[0].name()
    else:
        raise SystemExit("Can't find BatchNorm op after Conv op in Network.")
    # steps3: Find scale of bn
    score = None
    if bn_scale_param not in values:
        raise SystemExit("Can't find values of scales in BatchNorm.")
    value = values[bn_scale_param]
    score = np.abs(value.reshape([-1]))
    scores = {}
    for pruning_details in group.all_pruning_details():
        name = pruning_details.name
        axis = pruning_details.axis
        if name not in scores:
            scores[name] = {}
        scores[name][axis] = score
    return scores