code: def momentum_schedule(momentum, epoch_size=None, minibatch_size=None):
      """
      Create a momentum schedule (using the same semantics as
      :func:`learning_parameter_schedule`) which applies the momentum
      decay every N samples where N is specified by the argument `minibatch_size`.
  
      Args:
          momentum (float or list): see parameter ``schedule`` in
           :func:`training_parameter_schedule`.
          epoch_size (int): see parameter ``epoch_size`` in
           :func:`training_parameter_schedule`.
          minibatch_size (int): an integer to specify the reference minibatch size;
            CNTK will scale the momentum internally so as to simulate the momentum decay of the specified minibatch
            size while the actual minibatch sizes of the fed data can vary. In this way, momentum values can be provided
            in a minibatch-size agnostic way (equal decay per sample). If minibatch_size is `None` (default), the momentum
            is applied to the whole minibatch regardless of the actual minibatch sizes (not in a minibatch-size agnostic way).
  
      Examples:
          >>> # Use a fixed momentum of 0.99 for all samples
          >>> m = momentum_schedule(0.99)
  
          >>> # Use the momentum value 0.99 for the first 1000 samples,
          >>> # then 0.9 for the remaining ones
          >>> m = momentum_schedule([0.99,0.9], 1000)
          >>> m[0], m[999], m[1000], m[1001]
          (0.99, 0.99, 0.9, 0.9)
  
          >>> # Use the momentum value 0.99 for the first 999 samples,
          >>> # then 0.88 for the next 888 samples, and 0.77 for the
          >>> # the remaining ones
          >>> m = momentum_schedule([(999,0.99),(888,0.88),(0, 0.77)])
          >>> m[0], m[998], m[999], m[999+888-1], m[999+888]
          (0.99, 0.99, 0.88, 0.88, 0.77)
  
      Returns:
          momentum schedule
      """
      return learning_parameter_schedule(
          momentum, minibatch_size, epoch_size
      )
  
imports: from enum import Enum, unique
  import warnings
  import numpy
  import cntk.internal.utils
  from ..None import cntk_py, NDArrayView, asarray
  from cntk.internal import typemap
  from ..internal.swig_helper import map_if_possible
label_desc: Use image node_id 10 for calling a global function with example usage: momentum_schedule(momentum, epoch_size, minibatch_size) and returns: learning_parameter_schedule