old_contents: import numpy
  
  
  class ShuffleOrderSampler(object):
  
      def __init__(self, seed=None):
          # Use a distinct RandomState in the thread
          # for deterministic random number generation.
          # To support 32-bit platform and numpy < 1.11,
          # the seed is taken in a verbose manner.
          if seed is None:
              seed = numpy.asscalar(
                  numpy.random.randint(-(1 << 31), 1 << 31, 1).astype('uint32'))
          self._random = numpy.random.RandomState(seed)
  
      def __call__(self, current_order, current_position):
          """The order sampler of an iterator when shuffle option is True.
  
          Args:
              current_order (numpy.ndarray): 1-D array of indices.
                  The length should be the same as the dataset to sample
                  data from.
              current_position (int): The current position of an iterator.
  
          Returns:
              numpy.ndarray: 1-D array of indices. This is the order in which
                  examples are sampled from a dataset in the next epoch.
  
          """
          return self._random.permutation(len(current_order))
  
  
  def no_shuffle_order_sampler(current_order, current_position):
      """The default order sampler of an iterator when shuffle option is False.
  
      Args:
          current_order (numpy.ndarray): 1-D array of indices.
              The length should be the same as the dataset to sample
              data from.
          current_position (int): The current position of an iterator.
  
      Returns:
          None:
  
      """
      return None
  
new_contents: import numpy
  
  
  class ShuffleOrderSampler(object):
  
      def __init__(self, seed=None):
          # Use a distinct RandomState in the thread
          # for deterministic random number generation.
          # To support 32-bit platform and numpy < 1.11,
          # the seed is taken in a verbose manner.
          if seed is None:
              seed = numpy.random.randint(2**31)
          self._random = numpy.random.RandomState(seed)
  
      def __call__(self, current_order, current_position):
          """The order sampler of an iterator when shuffle option is True.
  
          Args:
              current_order (numpy.ndarray): 1-D array of indices.
                  The length should be the same as the dataset to sample
                  data from.
              current_position (int): The current position of an iterator.
  
          Returns:
              numpy.ndarray: 1-D array of indices. This is the order in which
                  examples are sampled from a dataset in the next epoch.
  
          """
          return self._random.permutation(len(current_order))
  
  
  def no_shuffle_order_sampler(current_order, current_position):
      """The default order sampler of an iterator when shuffle option is False.
  
      Args:
          current_order (numpy.ndarray): 1-D array of indices.
              The length should be the same as the dataset to sample
              data from.
          current_position (int): The current position of an iterator.
  
      Returns:
          None:
  
      """
      return None
  
subject: simplify seed selection