old_contents: import torch
  from torch import Tensor
  from torch import nn
  from typing import Dict
  import os
  import json
  
  
  class Dropout(nn.Module):
      """Dropout layer.
  
      :param dropout: Sets a dropout value for dense layer.
      """
      def __init__(self, dropout: float = 0.2):
          super(Dropout, self).__init__()
          self.dropout = dropout
          self.dropout_layer = nn.Dropout(self.dropout)
  
      def forward(self, features: Dict[str, Tensor]):
          features.update({'sentence_embedding': self.dropout_layer(features['sentence_embedding'])})
          return features
  
      def save(self, output_path):
          with open(os.path.join(output_path, 'config.json'), 'w') as fOut:
              json.dump({'dropout': self.dropout}, fOut)
  
          torch.save(self.state_dict(), os.path.join(output_path, 'pytorch_model.bin'))
  
      @staticmethod
      def load(input_path):
          with open(os.path.join(input_path, 'config.json')) as fIn:
              config = json.load(fIn)
  
          model = Dropout(**config)
          model.load_state_dict(torch.load(os.path.join(input_path, 'pytorch_model.bin'), map_location=torch.device('cpu')))
          return model
  
new_contents: import torch
  from torch import Tensor
  from torch import nn
  from typing import Dict
  import os
  import json
  
  
  class Dropout(nn.Module):
      """Dropout layer.
  
      :param dropout: Sets a dropout value for dense layer.
      """
      def __init__(self, dropout: float = 0.2):
          super(Dropout, self).__init__()
          self.dropout = dropout
          self.dropout_layer = nn.Dropout(self.dropout)
  
      def forward(self, features: Dict[str, Tensor]):
          features.update({'sentence_embedding': self.dropout_layer(features['sentence_embedding'])})
          return features
  
      def save(self, output_path):
          with open(os.path.join(output_path, 'config.json'), 'w') as fOut:
              json.dump({'dropout': self.dropout}, fOut)
  
  
  
      @staticmethod
      def load(input_path):
          with open(os.path.join(input_path, 'config.json')) as fIn:
              config = json.load(fIn)
  
          model = Dropout(**config)
          return model
  
subject: remove saving of model for Dropout - It does not have any parameters