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 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.unsqueeze(-1).unsqueeze(
          -1
      ) * x + self.bias.unsqueeze(-1).unsqueeze(-1)
      return x
  
imports: from torch import nn
  import torch
label_desc: Use image node_id 2 for calling the LayerNormChannel obj's underlying member method code with example usage: obj.forward(x) and returns: x