old_contents: #!/usr/bin/env python
  # encoding: utf-8
  
  # The MIT License (MIT)
  
  # Copyright (c) 2020 CNRS
  
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  # AUTHORS
  # Hervé BREDIN - http://herve.niderb.fr
  
  import torch
  import torch.nn as nn
  
  try:
      from typing import Literal
  except ImportError as e:
      from typing_extensions import Literal
  
  
  class Scaling(nn.Module):
      """Scale feature vectors
  
      Parameters
      ----------
      n_features : int
          Number of input features.
      method : {"unit", "logistic"}, optional
          Defaults to no scaling.
      """
  
      def __init__(self, n_features: int, method: Literal["fixed", "logistic"] = None):
          super().__init__()
          self.n_features = n_features
          self.method = method
  
          if self.method == "logistic":
              self.linear = nn.Linear(1, 1, bias=True)
              self.activation = nn.Sigmoid()
  
      def forward(self, features: torch.Tensor) -> torch.Tensor:
          """Scale features
  
          Parameters
          ----------
          features : torch.Tensor
              Input features of shape (batch_size, *, n_features)
  
          Returns
          -------
          scaled : torch.Tensor
              Scaled features of shape (batch_size, *, n_features)
          """
  
          if self.method is None:
              return features
  
          norm = features.norm(p=2, dim=-1, keepdim=True)
  
          if self.method == "unit":
              new_norm = 1.0
  
          if self.method == "logistic":
              new_norm = self.activation(self.linear(norm))
  
          return new_norm / (norm + 1e-6) * features
  
      @property
      def dimension(self):
          return self.n_features
  
new_contents: #!/usr/bin/env python
  # encoding: utf-8
  
  # The MIT License (MIT)
  
  # Copyright (c) 2020 CNRS
  
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  # AUTHORS
  # Hervé BREDIN - http://herve.niderb.fr
  
  import torch
  import torch.nn as nn
  
  try:
      from typing import Literal
  except ImportError as e:
      from typing_extensions import Literal
  
  
  class Scaling(nn.Module):
      """Scale feature vectors
  
      Parameters
      ----------
      n_features : int
          Number of input features.
      method : {"unit", "logistic"}, optional
          Defaults to no scaling.
      """
  
      def __init__(self, n_features: int, method: Literal["fixed", "logistic"] = None):
          super().__init__()
          self.n_features = n_features
          self.method = method
  
          if self.method == "logistic":
              self.batch_norm = nn.BatchNorm1d(
                  1, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True
              )
              self.activation = nn.Sigmoid()
  
      def forward(self, features: torch.Tensor) -> torch.Tensor:
          """Scale features
  
          Parameters
          ----------
          features : torch.Tensor
              Input features of shape (batch_size, *, n_features)
  
          Returns
          -------
          scaled : torch.Tensor
              Scaled features of shape (batch_size, *, n_features)
          """
  
          if self.method is None:
              return features
  
          norm = features.norm(p=2, dim=-1, keepdim=True)
  
          if self.method == "unit":
              new_norm = 1.0
  
          if self.method == "logistic":
              new_norm = self.activation(self.batch_norm(norm))
  
          return new_norm / (norm + 1e-6) * features
  
      @property
      def dimension(self):
          return self.n_features
  
subject: add batch-normalization in norm scaling