class_docstr: Cosine.
class_docstr_tok: ["Cosine","."]
docstr: Returns a `Cosine` instance.
  
  Args:
    decay_steps: Number of steps to decay over.
    alpha: Minimum learning rate value as a fraction of learning_rate.
  
  Returns:
    A `Cosine` instance.
docstr_tok: ["Returns","a","`","Cosine","`","instance",".","Args",":","decay_steps",":","Number","of","steps","to","decay","over",".","alpha",":","Minimum","learning","rate","value","as","a","fraction","of","learning_rate",".","Returns",":","A","`","Cosine","`","instance","."]
code: def __init__(self, decay_steps, alpha):
      """Returns a `Cosine` instance.
  
      Args:
        decay_steps: Number of steps to decay over.
        alpha: Minimum learning rate value as a fraction of learning_rate.
  
      Returns:
        A `Cosine` instance.
      """
  
      self._decay_fn = functools.partial(
          tf.train.cosine_decay, decay_steps=decay_steps, alpha=alpha
      )
  
file_path: adanet/research/improve_nas/trainer/optimizer.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import functools
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new Cosine object from inherited base classes: LearningRateSchedule with example: obj = Cosine(decay_steps, alpha)