old_contents: import torch
  from torch import Tensor
  from torch import nn
  from torch import functional as F
  from typing import Union, Tuple, List, Iterable, Dict
  import os
  import json
  from ..util import fullname, import_from_string
  
  
  class Dense(nn.Module):
      """Feed-forward function with  activiation function.
  
      This layer takes a fixed-sized sentence embedding and passes it through a feed-forward layer. Can be used to generate deep averaging networs (DAN).
  
      :param in_features: Size of the input dimension
      :param out_features: Output size
      :param bias: Add a bias vector
      :param activation_function: Pytorch activation function applied on output
      :param init_weight: Initial value for the matrix of the linear layer
      :param init_bias: Initial value for the bias of the linear layer
      """
      def __init__(self, in_features: int, out_features: int, bias: bool = True, activation_function=nn.Tanh(), init_weight: Tensor = None, init_bias: Tensor = None):
          super(Dense, self).__init__()
          self.in_features = in_features
          self.out_features = out_features
          self.bias = bias
          self.activation_function = activation_function
          self.linear = nn.Linear(in_features, out_features, bias=bias)
  
          if init_weight is not None:
              self.linear.weight = nn.Parameter(init_weight)
  
          if init_bias is not None:
              self.linear.bias = nn.Parameter(init_bias)
  
      def forward(self, features: Dict[str, Tensor]):
          features.update({'sentence_embedding': self.activation_function(self.linear(features['sentence_embedding']))})
          return features
  
      def get_sentence_embedding_dimension(self) -> int:
          return self.out_features
  
      def get_config_dict(self):
          return {'in_features': self.in_features, 'out_features': self.out_features, 'bias': self.bias, 'activation_function': fullname(self.activation_function)}
  
      def save(self, output_path):
          with open(os.path.join(output_path, 'config.json'), 'w') as fOut:
              json.dump(self.get_config_dict(), fOut)
  
          torch.save(self.state_dict(), os.path.join(output_path, 'pytorch_model.bin'))
  
      def __repr__(self):
          return "Dense({})".format(self.get_config_dict())
  
      @staticmethod
      def load(input_path):
          with open(os.path.join(input_path, 'config.json')) as fIn:
              config = json.load(fIn)
  
          config['activation_function'] = import_from_string(config['activation_function'])()
          model = Dense(**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 torch import functional as F
  from typing import Union, Tuple, List, Iterable, Dict
  import os
  import json
  from ..util import fullname, import_from_string
  
  
  class Dense(nn.Module):
      """Feed-forward function with  activiation function.
  
      This layer takes a fixed-sized sentence embedding and passes it through a feed-forward layer. Can be used to generate deep averaging networks (DAN).
  
      :param in_features: Size of the input dimension
      :param out_features: Output size
      :param bias: Add a bias vector
      :param activation_function: Pytorch activation function applied on output
      :param init_weight: Initial value for the matrix of the linear layer
      :param init_bias: Initial value for the bias of the linear layer
      """
      def __init__(self, in_features: int, out_features: int, bias: bool = True, activation_function=nn.Tanh(), init_weight: Tensor = None, init_bias: Tensor = None):
          super(Dense, self).__init__()
          self.in_features = in_features
          self.out_features = out_features
          self.bias = bias
          self.activation_function = activation_function
          self.linear = nn.Linear(in_features, out_features, bias=bias)
  
          if init_weight is not None:
              self.linear.weight = nn.Parameter(init_weight)
  
          if init_bias is not None:
              self.linear.bias = nn.Parameter(init_bias)
  
      def forward(self, features: Dict[str, Tensor]):
          features.update({'sentence_embedding': self.activation_function(self.linear(features['sentence_embedding']))})
          return features
  
      def get_sentence_embedding_dimension(self) -> int:
          return self.out_features
  
      def get_config_dict(self):
          return {'in_features': self.in_features, 'out_features': self.out_features, 'bias': self.bias, 'activation_function': fullname(self.activation_function)}
  
      def save(self, output_path):
          with open(os.path.join(output_path, 'config.json'), 'w') as fOut:
              json.dump(self.get_config_dict(), fOut)
  
          torch.save(self.state_dict(), os.path.join(output_path, 'pytorch_model.bin'))
  
      def __repr__(self):
          return "Dense({})".format(self.get_config_dict())
  
      @staticmethod
      def load(input_path):
          with open(os.path.join(input_path, 'config.json')) as fIn:
              config = json.load(fIn)
  
          config['activation_function'] = import_from_string(config['activation_function'])()
          model = Dense(**config)
          model.load_state_dict(torch.load(os.path.join(input_path, 'pytorch_model.bin'), map_location=torch.device('cpu')))
          return model
  
subject: fix docs typo