class_docstr: Sequence-to-sequence base class.
class_docstr_tok: ["Sequence-to-sequence","base","class","."]
docstr: Get learning rate decay.
docstr_tok: ["Get","learning","rate","decay","."]
code: def _get_learning_rate_decay(self, hparams):
      """Get learning rate decay."""
      (
          start_decay_step,
          decay_steps,
          decay_factor,
      ) = self._get_decay_info(hparams)
      utils.print_out(
          "  decay_scheme=%s, start_decay_step=%d, decay_steps %d, "
          "decay_factor %g"
          % (
              hparams.decay_scheme,
              start_decay_step,
              decay_steps,
              decay_factor,
          )
      )
  
      return tf.cond(
          self.global_step < start_decay_step,
          lambda: self.learning_rate,
          lambda: tf.train.exponential_decay(
              self.learning_rate,
              (self.global_step - start_decay_step),
              decay_steps,
              decay_factor,
              staircase=True,
          ),
          name="learning_rate_decay_cond",
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import collections
  import tensorflow
  from tensorflow.contrib.model_pruning import get_masks, get_thresholds
  from tensorflow.contrib.model_pruning.python import pruning
  from tensorflow.contrib.model_pruning.python.layers import core_layers
  from .None import model_helper
  from .utils import misc_utils, iterator_utils
label_desc: Use image node_id 6 for calling the BaseModel obj's underlying member method code with example usage: obj._get_learning_rate_decay(hparams) and returns: tf