code: def forward(self, x):
      # Handle higher dimension logic
      shape = x.shape
      if self.transposed:
          x = rearrange(x, "b d ... -> b d (...)")
      else:
          x = rearrange(x, "b ... d -> b (...)d ")
  
      # The cases of LayerNorm / no normalization
      # are automatically handled in all cases
      # Instance/Batch Norm work automatically with transposed axes
      if self.channel or self.transposed:
          x = self.norm(x)
      else:
          x = x.transpose(-1, -2)
          x = self.norm(x)
          x = x.transpose(-1, -2)
  
      x = x.view(shape)
      return x
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange
  from opt_einsum import contract
label_desc: Use image node_id 2 for calling the Normalization obj's underlying member method code with example usage: obj.forward(x) and returns: x