def __init__(self, in_channels=3, out_channels=64, norm="BN"):
        """
        Args:
            norm (str or callable): norm after the first conv layer.
                See :func:`layers.get_norm` for supported format.
        """
        super().__init__(in_channels, out_channels, 4)
        self.in_channels = in_channels
        self.conv1 = nn.Sequential(
            Conv2d(
                in_channels,
                32,
                kernel_size=3,
                stride=2,
                padding=1,
                bias=False,
                ),
            get_norm(norm, 32),
            nn.ReLU(inplace=True),
            Conv2d(
                32,
                32,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False,
                ),
            get_norm(norm, 32),
            nn.ReLU(inplace=True),
            Conv2d(
                32,
                out_channels,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False,
                ),
        )
        self.bn1 = get_norm(norm, out_channels)

        for layer in self.conv1:
            if isinstance(layer, Conv2d):
                weight_init.c2_msra_fill(layer)

        Args:
            norm (str or callable): norm after the first conv layer.
                See :func:`layers.get_norm` for supported format.
        
video_chat_with_StableLM/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/res2net.py