class_docstr: An optimizer that applies loss scaling.
  
  Loss scaling is a process that multiplies the loss by a multiplier called the
  loss scale, and divides each gradient by the same multiplier. The pseudocode
  for this process is:
  
  ```
  loss = ...
  loss *= loss_scale
  grads = gradients(loss, vars)
  grads /= loss_scale
  ```
  
  Mathematically, loss scaling has no effect, but can help avoid numerical
  underflow in intermediate gradients when float16 tensors are used for mixed
  precision training. By multiplying the loss, each intermediate gradient will
  have the same multiplier applied.
  
  The loss scale can either be a fixed constant, chosen by the user, or be
  dynamically determined. Dynamically determining the loss scale is convenient
  as a loss scale does not have to be explicitly chosen. However it reduces
  performance.
  
  This optimizer wraps another optimizer and applies loss scaling to it via a
  `LossScale`. Loss scaling is applied whenever gradients are
  computed, such as through `minimize()`.
docstr: Returns the variables of the Optimizer.
docstr_tok: ["Returns","the","variables","of","the","Optimizer","."]
code: def variables(self):
      """Returns the variables of the Optimizer."""
      return self._optimizer.variables() + list(
          self._loss_scale._weights.values()
      )
  
imports: from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import smart_cond
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import optimizer
  from tensorflow.python.training.experimental import loss_scale
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 14 for calling the MixedPrecisionLossScaleOptimizer obj's underlying member method code with example usage: obj.variables() and returns: unknown