def METHOD_NAME(m):
    prev_previous_type = nn.Identity()
    prev_previous_name = ''
    previous_type = nn.Identity()
    previous_name = ''
    for name, module in m.named_modules():
        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
            # print("FUSED ", prev_previous_name, previous_name, name)
            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
            # print("FUSED ", prev_previous_name, previous_name)
            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
        #    print("FUSED ", previous_name, name)
        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
        prev_previous_type = previous_type
        prev_previous_name = previous_name
        previous_type = type(module)
        previous_name = nam