old_contents: # MIT License
  #
  # 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.
  
  
  import os
  import random
  
  import torch
  
  
  def create_rng_for_worker():
      """Create worker-specific random number generator
  
      This makes sure that
      1. training samples generation is reproducible
      2. all workers use a different seed (e.g. to avoid training samples duplication)
      """
  
      # create random number generator
      rng = random.Random()
  
      #  create seed as a combination of PL_GLOBAL_SEED (set by pl.seed_everything())
      #  and current worker id.
      global_seed = int(os.environ.get("PL_GLOBAL_SEED", 1))
      worker_info = torch.utils.data.get_worker_info()
      seed = global_seed + worker_info.id
  
      # seed
      rng.seed(seed)
  
      return rng
  
new_contents: # MIT License
  #
  # 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.
  
  
  import os
  import random
  
  import torch
  
  
  def create_rng_for_worker():
      """Create worker-specific random number generator
  
      This makes sure that
      1. training samples generation is reproducible
      2. all workers use a different seed (e.g. to avoid training samples duplication)
      """
  
      # create random number generator
      rng = random.Random()
  
      #  create seed as a combination of PL_GLOBAL_SEED (set by pl.seed_everything())
      #  and current worker id.
      global_seed = int(os.environ.get("PL_GLOBAL_SEED", 1))
      worker_info = torch.utils.data.get_worker_info()
      if worker_info is None:
          rng.seed(global_seed)
      else:
          rng.seed(global_seed + worker_info.id)
  
      return rng
  
subject: fix corner case when num_worker is set to 0 (#59)