class_docstr: Adafactor.
code: def _learning_rate_default(self, multiply_by_parameter_scale):
      step_num = tf.cast(
          tf.train.get_or_create_global_step(), tf.float32
      )
      learning_rate = tf.minimum(tf.math.rsqrt(step_num + 1.0), 0.01)
      if (
          not multiply_by_parameter_scale
          and not layers.unit_scaling_convention()
      ):
          learning_rate *= 0.05
      return learning_rate
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import re
  import gin
  from mesh_tensorflow import layers
  from mesh_tensorflow import ops_with_redefined_builtins
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling the AdafactorOptimizer obj's underlying member method code with example usage: obj._learning_rate_default(multiply_by_parameter_scale) and returns: learning_rate