code: def forward(self, z):
      if z.size(-1) == 1:
          return z
  
      mu = torch.mean(z, keepdim=True, dim=-1)
      sigma = torch.std(z, keepdim=True, dim=-1)
      ln_out = (z - mu.expand_as(z)) / (sigma.expand_as(z) + self.eps)
      if self.affine:
          ln_out = ln_out * self.a_2.expand_as(
              ln_out
          ) + self.b_2.expand_as(ln_out)
  
      return ln_out
  
imports: import numpy
  import functools
  import sys
  import torch
  from torch.autograd import Variable
  import torch.nn
  import torch.nn.init
label_desc: Use image node_id 2 for calling the LayerNormalization obj's underlying member method code with example usage: obj.forward(z) and returns: ln_out, z