class_docstr: A LayerNorm variant, popularized by Transformers, that performs
  pointwise mean and variance normalization over the channel dimension for
  inputs that have shape (batch_size, channels, height, width).
class_docstr_tok: ["A","LayerNorm","variant",",","popularized","by","Transformers",",","that","performs","pointwise","mean","and","variance","normalization","over","the","channel","dimension","for","inputs","that","have","shape","(","batch_size",",","channels",",","height",",","width",")","."]
code: def forward(self, x):
      u = x.mean(1, keepdim=True)
      s = (x - u).pow(2).mean(1, keepdim=True)
      x = (x - u) / torch.sqrt(s + self.eps)
      x = self.weight[:, None, None] * x + self.bias[:, None, None]
      return x
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import build_activation_layer, build_norm_layer
  from mmcv.cnn.bricks import DropPath
  from mmengine.logging import MMLogger
  from mmengine.model import BaseModule
  from mmengine.runner.checkpoint import CheckpointLoader
  from mmdet.registry import MODELS
label_desc: Use image node_id 2 for calling the LN2d obj's underlying member method code with example usage: obj.forward(x) and returns: x