class_docstr: LayerNorm only for Channel Dimension.
  Input: tensor in shape [B, C, H, W]
  Args:
      num_channels (int): number of channel
      eps (float): precision
class_docstr_tok: ["LayerNorm","only","for","Channel","Dimension",".","Input",":","tensor","in","shape","[","B",",","C",",","H",",","W","]","Args",":","num_channels","(","int",")",":","number","of","channel","eps","(","float",")",":","precision"]
code: def __init__(self, num_channels, eps=1e-05):
      super().__init__()
      self.weight = nn.Parameter(torch.ones(num_channels))
      self.bias = nn.Parameter(torch.zeros(num_channels))
      self.eps = eps
  
imports: from torch import nn
  import torch
label_desc: Use image node_id 1 to create a new LayerNormChannel object from inherited base classes: nn with example: obj = LayerNormChannel(num_channels, eps)