code: def forward(self, x):
      cum_mean = x.mean(dim=1, keepdim=True)
      cum_var = x.var(dim=1, keepdim=True)
      x = (x - cum_mean) * torch.rsqrt(cum_var + self.epsilon)
      if self.weight is not None:
          x = x * self.weight + self.bias
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the ChannelNorm obj's underlying member method code with example usage: obj.forward(x) and returns: x