old_contents: # MIT License
  #
  # Copyright (c) 2021 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.
  
  
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import CyclicLR as _CyclicLR
  
  
  def CyclicLR(
      optimizer: Optimizer,
      min_lr: float = 1e-8,
      max_lr: float = 1e-3,
      step_size_up: int = 50000,
      **kwargs,
  ):
      """Wrapper around CyclicLR learning rate scheduler
  
      Parameters
      ----------
      optimizer : Optimizer
          Optimizer
      min_lr : float, optional
          Defaults to 1e-8.
      max_lr : float, optional
          Defaults to 1e-3
      step_size_up : int, optional
          Number of training iterations in the increasing half of a cycle.
          Defaults to 50000.
      """
  
      return {
          "scheduler": _CyclicLR(
              optimizer,
              base_lr=min_lr,
              max_lr=max_lr,
              step_size_up=step_size_up,
              mode="triangular2",
              cycle_momentum=False,
          ),
          "interval": "step",
      }
  
new_contents: # MIT License
  #
  # Copyright (c) 2021 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.
  
  
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import CyclicLR as _CyclicLR
  
  
  def CyclicLR(
      optimizer: Optimizer,
      min_lr: float = 1e-8,
      max_lr: float = 1e-3,
      step_size_up: int = 50000,
      mode: str = "triangular",
      **kwargs,
  ):
      """Wrapper around CyclicLR learning rate scheduler
  
      Parameters
      ----------
      optimizer : Optimizer
          Optimizer
      min_lr : float, optional
          Defaults to 1e-8.
      max_lr : float, optional
          Defaults to 1e-3
      step_size_up : int, optional
          Number of training iterations in the increasing half of a cycle.
          Defaults to 50000.
      mode : {"triangular", "triangular2"}, optional
          Defaults to "triangular".
      """
  
      return {
          "scheduler": _CyclicLR(
              optimizer,
              base_lr=min_lr,
              max_lr=max_lr,
              step_size_up=step_size_up,
              mode=mode,
              cycle_momentum=False,
          ),
          "interval": "step",
      }
  
subject: switch to triangular