class_docstr: Sequence-to-sequence base class.
class_docstr_tok: ["Sequence-to-sequence","base","class","."]
docstr: Return decay info based on decay_scheme.
docstr_tok: ["Return","decay","info","based","on","decay_scheme","."]
code: def _get_decay_info(self, hparams):
      """Return decay info based on decay_scheme."""
      if hparams.decay_scheme in ["luong5", "luong10", "luong234"]:
          decay_factor = 0.5
          if hparams.decay_scheme == "luong5":
              start_decay_step = int(hparams.num_train_steps / 2)
              decay_times = 5
          elif hparams.decay_scheme == "luong10":
              start_decay_step = int(hparams.num_train_steps / 2)
              decay_times = 10
          elif hparams.decay_scheme == "luong234":
              start_decay_step = int(hparams.num_train_steps * 2 / 3)
              decay_times = 4
          remain_steps = hparams.num_train_steps - start_decay_step
          decay_steps = int(remain_steps / decay_times)
      elif not hparams.decay_scheme:  # no decay
          start_decay_step = hparams.num_train_steps
          decay_steps = 0
          decay_factor = 1.0
      elif hparams.decay_scheme:
          raise ValueError(
              "Unknown decay scheme %s" % hparams.decay_scheme
          )
      return start_decay_step, decay_steps, decay_factor
  
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 5 for calling the BaseModel obj's underlying member method code with example usage: obj._get_decay_info(hparams) and returns: start_decay_step,  decay_steps,  decay_factor