old_contents: import torch
  from torch import Tensor
  from torch import nn
  from typing import Dict
  import os
  import json
  import torch.nn.functional as F
  
  class Normalize(nn.Module):
      """
      This layer normalizes embeddings to unit length
      """
      def __init__(self):
          super(Normalize, self).__init__()
  
      def forward(self, features: Dict[str, Tensor]):
          features.update({'sentence_embedding': F.normalize(features['sentence_embedding'], p=2, dim=1)})
          return features
  
      def save(self, output_path):
          pass
  
      @staticmethod
      def load(input_path):
          return Normalize()
  
new_contents: from torch import Tensor
  from torch import nn
  from typing import Dict
  import torch.nn.functional as F
  
  class Normalize(nn.Module):
      """
      This layer normalizes embeddings to unit length
      """
      def __init__(self):
          super(Normalize, self).__init__()
  
      def forward(self, features: Dict[str, Tensor]):
          features.update({'sentence_embedding': F.normalize(features['sentence_embedding'], p=2, dim=1)})
          return features
  
      def save(self, output_path):
          pass
  
      @staticmethod
      def load(input_path):
          return Normalize()
  
subject: remove unneeded imports