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.
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docstr: Resets the learning rate. The new schedule is adjusted to be relative
  to the current number of elapsed samples/sweeps: the 0 offset in
  the new schedule corresponds to the current value of elapsed samples/sweeps,
  and it takes effect from the current position in the training process onwards.
  
  Args:
      learning_rate (output of :func:`learning_parameter_schedule`)
       learning rate to reset to
code: def reset_learning_rate(self, learning_rate):
      """
      Resets the learning rate. The new schedule is adjusted to be relative
      to the current number of elapsed samples/sweeps: the 0 offset in
      the new schedule corresponds to the current value of elapsed samples/sweeps,
      and it takes effect from the current position in the training process onwards.
  
      Args:
          learning_rate (output of :func:`learning_parameter_schedule`)
           learning rate to reset to
      """
      _verify_learning_rate_type(learning_rate)
      if not learning_rate.is_minibatch_size_explicitly_specified:
          # If the schedule minibatch size is not explicitly specified, the learner's specification will take over
          if (
              self.minibatch_size is not None
              and self.minibatch_size != self.ignored_minibatch_size
          ):
              learning_rate.minibatch_size = self.minibatch_size
      return super(Learner, self).reset_learning_rate(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 3 for calling the Learner obj's underlying member method code with example usage: obj.reset_learning_rate(learning_rate) and returns: super