parameters: (schedule, epoch_size=None)
return_statement: return learning_parameter_schedule(schedule, minibatch_size=1, epoch_size=epoch_size)
docstring: Create a learning parameter schedule as if the parameter is applied to minibatches of size 1. CNTK
      will scale the parameters accordingly with respect to the actual minibatch size.
  
      Args:
          schedule (float or list): if float, is the parameter schedule to be used
           for all samples. In case of list [p_1, p_2, .., p_n], the i-th parameter p_i in the list is used as the
           value from the (``epoch_size`` * (i-1) + 1)-th sample to the (``epoch_size`` * i)-th sample. If list contains
           pair, i.e. [(num_epoch_1, p_1), (num_epoch_n, p_2), .., (num_epoch_n, p_n)], the i-th parameter is used as a
           value from the (``epoch_size`` * (num_epoch_0 + ... + num_epoch_2 + ... + num_epoch_(i-1) + 1)-th sample to the
           (``epoch_size`` * num_epoch_i)-th sample (taking num_epoch_0 = 0 as a special initialization).
          epoch_size (optional, int): number of samples as a scheduling unit.
           Parameters in the schedule change their values every ``epoch_size``
           samples. If no ``epoch_size`` is provided, this parameter is substituted
           by the size of the full data sweep, in which case the scheduling unit is
           the entire data sweep (as indicated by the MinibatchSource) and parameters
           change their values on the sweep-by-sweep basis specified by the
           ``schedule``.
  
      Returns:
          learning parameter schedule as if it is applied to minibatches of size 1.
docstring_summary: Create a learning parameter schedule as if the parameter is applied to minibatches of size 1. CNTK
      will scale the parameters accordingly with respect to the actual minibatch size.
docstring_tokens: Create a learning parameter schedule as if the parameter is applied to minibatches of size 1 . CNTK will scale the parameters accordingly with respect to the actual minibatch size .
function: def learning_parameter_schedule_per_sample(schedule, epoch_size=None):
      '''
      Create a learning parameter schedule as if the parameter is applied to minibatches of size 1. CNTK
      will scale the parameters accordingly with respect to the actual minibatch size.
  
      Args:
          schedule (float or list): if float, is the parameter schedule to be used
           for all samples. In case of list [p_1, p_2, .., p_n], the i-th parameter p_i in the list is used as the
           value from the (``epoch_size`` * (i-1) + 1)-th sample to the (``epoch_size`` * i)-th sample. If list contains
           pair, i.e. [(num_epoch_1, p_1), (num_epoch_n, p_2), .., (num_epoch_n, p_n)], the i-th parameter is used as a
           value from the (``epoch_size`` * (num_epoch_0 + ... + num_epoch_2 + ... + num_epoch_(i-1) + 1)-th sample to the
           (``epoch_size`` * num_epoch_i)-th sample (taking num_epoch_0 = 0 as a special initialization).
          epoch_size (optional, int): number of samples as a scheduling unit.
           Parameters in the schedule change their values every ``epoch_size``
           samples. If no ``epoch_size`` is provided, this parameter is substituted
           by the size of the full data sweep, in which case the scheduling unit is
           the entire data sweep (as indicated by the MinibatchSource) and parameters
           change their values on the sweep-by-sweep basis specified by the
           ``schedule``.
  
      Returns:
          learning parameter schedule as if it is applied to minibatches of size 1.
      '''
      return learning_parameter_schedule(schedule, minibatch_size=1, epoch_size=epoch_size)
function_tokens: def learning_parameter_schedule_per_sample ( schedule , epoch_size = None ) : return learning_parameter_schedule ( schedule , minibatch_size = 1 , epoch_size = epoch_size )