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 __init__(self, normalized_shape, eps=1e-6):
      super().__init__()
      self.weight = self.create_parameter([normalized_shape])
      self.bias = self.create_parameter([normalized_shape])
      self.eps = eps
      self.normalized_shape = (normalized_shape,)
  
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 1 to create a new LayerNorm object from inherited base classes: nn with example: obj = LayerNorm(normalized_shape, eps)