code: def __init__(self, in_channels, eps=1e-5, affine=True):
      super().__init__()
      self.in_channels = in_channels
      self.eps = eps
  
      if affine:
          self.weight = Parameter(torch.Tensor(in_channels))
          self.bias = Parameter(torch.Tensor(in_channels))
      else:
          self.register_parameter("weight", None)
          self.register_parameter("bias", None)
  
      self.reset_parameters()
  
imports: import copy
  import dgl
  import torch
  from dgl.nn.pytorch.conv import GraphConv, SAGEConv
  from torch import nn
  from torch.nn import BatchNorm1d, Parameter
  from torch.nn.init import ones_, zeros_
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: nn with example: obj = LayerNorm(in_channels, eps, affine)