def __init__(
        self,
        in_channels,
        out_channels,
        bottleneck_channels,
        norm="LN",
        act_layer=nn.GELU,
    ):
        """
        Args:
            in_channels (int): Number of input channels.
            out_channels (int): Number of output channels.
            bottleneck_channels (int): number of output channels for the 3x3
                "bottleneck" conv layers.
            norm (str or callable): normalization for all conv layers.
                See :func:`layers.get_norm` for supported format.
            act_layer (callable): activation for all conv layers.
        """
        super().__init__(in_channels, out_channels, 1)

        self.conv1 = Conv2d(in_channels, bottleneck_channels, 1, bias=False)
        self.norm1 = get_norm(norm, bottleneck_channels)
        self.act1 = act_layer()

        self.conv2 = Conv2d(
            bottleneck_channels,
            bottleneck_channels,
            3,
            padding=1,
            bias=False,
        )
        self.norm2 = get_norm(norm, bottleneck_channels)
        self.act2 = act_layer()

        self.conv3 = Conv2d(bottleneck_channels, out_channels, 1, bias=False)
        self.norm3 = get_norm(norm, out_channels)

        for layer in [self.conv1, self.conv2, self.conv3]:
            weight_init.c2_msra_fill(layer)
        for layer in [self.norm1, self.norm2]:
            layer.weight.data.fill_(1.0)
            layer.bias.data.zero_()
        # zero init last norm layer.
        self.norm3.weight.data.zero_()
        self.norm3.bias.data.zero_()

        Args:
            in_channels (int): Number of input channels.
            out_channels (int): Number of output channels.
            bottleneck_channels (int): number of output channels for the 3x3
                "bottleneck" conv layers.
            norm (str or callable): normalization for all conv layers.
                See :func:`layers.get_norm` for supported format.
            act_layer (callable): activation for all conv layers.
        
EVA-01/det/detectron2/modeling/backbone/vit.py