class_docstr: Abstraction for learning a subset of parameters of a learnable function using first order gradient values.
  For example momentum, AdaGrad, RMSProp, etc. are different types of learners with their own algorithms for
  learning parameter values using first order gradients.
  To instantiate a concrete learner, use the factory methods in this module.
class_docstr_tok: ["Abstraction","for","learning","a","subset","of","parameters","of","a","learnable","function","using","first","order","gradient","values",".","For","example","momentum",",","AdaGrad",",","RMSProp",",","etc",".","are","different","types","of","learners","with","their","own","algorithms","for","learning","parameter","values","using","first","order","gradients",".","To","instantiate","a","concrete","learner",",","use","the","factory","methods","in","this","module","."]
docstr: Current learning rate schedule.
code: def learning_rate(self):
      """
      Current learning rate schedule.
      """
      return super(Learner, self).learning_rate()
  
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 4 for calling the Learner obj's underlying member method code with example usage: obj.learning_rate() and returns: super