def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
        """
        Multi-Scale Deformable Attention Module
        :param d_model      hidden dimension
        :param n_levels     number of feature levels
        :param n_heads      number of attention heads
        :param n_points     number of sampling points per attention head per feature level
        """
        super().__init__()
        if d_model % n_heads != 0:
            raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
        _d_per_head = d_model // n_heads
        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
        if not _is_power_of_2(_d_per_head):
            warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
                          "which is more efficient in our CUDA implementation.")

        self.im2col_step = 128

        self.d_model = d_model
        self.n_levels = n_levels
        self.n_heads = n_heads
        self.n_points = n_points

        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
        self.value_proj = nn.Linear(d_model, d_model)
        self.output_proj = nn.Linear(d_model, d_model)

        self._reset_parameters()

        Multi-Scale Deformable Attention Module
        :param d_model      hidden dimension
        :param n_levels     number of feature levels
        :param n_heads      number of attention heads
        :param n_points     number of sampling points per attention head per feature level
        
ControlNet-v1-1-nightly/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py