class_docstr: Optimizer wrapper providing deferred gradient application.
  
  Large hardware configurations are in high-demand, and difficult to come by.
  This class enables simulating execution on large hardware configurations, by
  accumulating gradients across multiple batches. A few caveats apply:
  
  * Batch statistics (e.g. batch norm) will continue to be based on the
   "micro-batch" size.
  * This effectively trades off computation/time: simulating a large cluster on
    a single core will take an excessive amount of time.
  
  N.B. Learning rate schedules may need to be adjusted in addition to using
  this optimizer. Schedules should either be scaled down by the relative batch
  size, or use a schedule based on the number of examples to be consistent
  across different batch sizes.
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code: def get_slot_names(self, *args, **kwargs):
      return self._opt.get_slot_names(*args, **kwargs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any, List
  from six.moves import zip
  import tensorflow.compat.v1
  from tensorflow.compiler.xla.experimental.xla_sharding import xla_sharding
label_desc: Use image node_id 11 for calling the GradientAggregationOptimizer obj's underlying member method code with example usage: obj.get_slot_names() and returns: self