class_docstr: Channel Mapper to reduce/increase channels of backbone features.
  
  This is used to reduce/increase channels of backbone features.
  
  Args:
      in_channels (List[int]): Number of input channels per scale.
      out_channels (int): Number of output channels (used at each scale).
      kernel_size (int, optional): kernel_size for reducing channels (used
          at each scale). Default: 3.
      conv_cfg (dict, optional): Config dict for convolution layer.
          Default: None.
      norm_cfg (dict, optional): Config dict for normalization layer.
          Default: None.
      act_cfg (dict, optional): Config dict for activation layer in
          ConvModule. Default: dict(type='ReLU').
      num_outs (int, optional): Number of output feature maps. There
          would be extra_convs when num_outs larger than the length
          of in_channels.
      init_cfg (dict or list[dict], optional): Initialization config dict.
      
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docstr: Initialize the transformer weights.
docstr_tok: ["Initialize","the","transformer","weights","."]
code: def init_weights(self):
      """Initialize the transformer weights."""
      for p in self.parameters():
          if p.rank() > 1:
              xavier_uniform_(p)
              if hasattr(p, "bias") and p.bias is not None:
                  constant_(p.bais)
  
file_path: PaddleDetection/ppdet/modeling/necks/channel_mapper.py
imports: import paddle.nn
  from ppdet.core.workspace import register, serializable
  from ..backbones.hrnet import ConvNormLayer
  from ..shape_spec import ShapeSpec
  from ..initializer import xavier_uniform_, constant_
label_desc: Use image node_id 4 for calling the ChannelMapper obj's underlying member method code with example usage: obj.init_weights() without return types