text: #!/usr/bin/env python3
  # -*- coding: utf-8 -*
  import torch
  import torch.nn as nn
  import numpy as np
  from copy import copy, deepcopy
  
  import sys
  sys.path.append('../') # or just install the module
  sys.path.append('../../fuzzy-torch') # or just install the module
  sys.path.append('../../fuzzy-tools') # or just install the module
  
  import torch
  import fuzzytorch.models.seq_utils as seq_utils
  
  embedding_dim = 128
  layer_norm = nn.LayerNorm([embedding_dim])
  
  n = 3
  x1 = torch.rand((n, 10, embedding_dim))
  x2 = torch.zeros((n, 20, embedding_dim))
  
  x = torch.cat([x1, x2], dim=1)
  print('x', x[0,:,0])
  x_norm = layer_norm(x)
  print('x_norm', x_norm[0,:,0])
  
  x = torch.cat([x1, x2, x2, x2, x1*100], dim=1)
  #x[:,:,-1] += 100
  # x[:,-1,:] += 100
  x[-1,:,:] += 100
  print('x', x[0,:,0])
  x_norm = layer_norm(x)
  print('x_norm', x_norm[0,:,0])
  