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: Update the parameters associated with this learner.
  
  Args:
      gradient_values (dict): maps :class:`~cntk.variables.Parameter` to
       a NumPy array containing the first order gradient values for the
       Parameter w.r.t. the training objective.
      training_sample_count (int): number of samples in the minibatch
      is_sweep_end (bool): a flag indicating whether it is at the end of a sweep of data
  
  Returns:
      bool: `False` to indicate that learning has stopped for all of the parameters associated with this learner
code: def update(
      self, gradient_values, training_sample_count, is_sweep_end=False
  ):
      """
      Update the parameters associated with this learner.
  
      Args:
          gradient_values (dict): maps :class:`~cntk.variables.Parameter` to
           a NumPy array containing the first order gradient values for the
           Parameter w.r.t. the training objective.
          training_sample_count (int): number of samples in the minibatch
          is_sweep_end (bool): a flag indicating whether it is at the end of a sweep of data
  
      Returns:
          bool: `False` to indicate that learning has stopped for all of the parameters associated with this learner
      """
      var_nd_map = {
          var: NDArrayView.from_data(val)
          for var, val in gradient_values.items()
      }
  
      return super(Learner, self)._update(
          var_nd_map, training_sample_count, is_sweep_end
      )
  
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 1 for calling the Learner obj's underlying member method code with example usage: obj.update(gradient_values, training_sample_count, is_sweep_end) and returns: super