class_docstr: A LayerNorm variant, popularized by Transformers, that performs point-wise mean and
  variance normalization over the channel dimension for inputs that have shape
  (batch_size, channels, height, width).    
  Note that, the modified LayerNorm on used in ResBlock and SimpleFeaturePyramid.
  
  In ViT, we use the nn.LayerNorm
class_docstr_tok: ["A","LayerNorm","variant",",","popularized","by","Transformers",",","that","performs","point-wise","mean","and","variance","normalization","over","the","channel","dimension","for","inputs","that","have","shape","(","batch_size",",","channels",",","height",",","width",")",".","Note","that",",","the","modified","LayerNorm","on","used","in","ResBlock","and","SimpleFeaturePyramid",".","In","ViT",",","we","use","the","nn.LayerNorm"]
code: def forward(self, x):
      u = x.mean(1, keepdim=True)
      s = (x - u).pow(2).mean(1, keepdim=True)
      x = (x - u) / paddle.sqrt(s + self.eps)
      x = self.weight[:, None, None] * x + self.bias[:, None, None]
      return x
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import math
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Constant, TruncatedNormal
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import zeros_, DropPath, Identity, window_partition, window_unpartition
  from ..initializer import linear_init_
label_desc: Use image node_id 2 for calling the LayerNorm obj's underlying member method code with example usage: obj.forward(x) and returns: x