class_docstr: Functional object that defines a shardable computation.
  
  This object defines functions required to create and manipulate data objects.
  These data objects, referred to below as 'accumulators', are computation-
  specific and may be implemented alongside concrete subclasses of Combiner
  (if necessary - some computations may be simple enough that standard Python
  types can be used as accumulators).
  
  The intent for this class is that by describing computations in this way, we
  can arbitrarily shard a dataset, perform computations on a subset, and then
  merge the computation into a final result. This enables distributed
  computation.
  
  The combiner itself does not own any state - all computational state is owned
  by the accumulator objects. This is so that we can have an arbitrary number of
  Combiners (thus sharding the computation N ways) without risking any change
  to the underlying computation. These accumulator objects are uniquely
  associated with each Combiner; a Combiner defines what the accumulator object
  should be and will only work with accumulators of that type.
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docstr: Deserialize an accumulator received from 'serialize()'.
  
  This function deserializes an accumulator serialized by 'serialize()'.
  
  Args:
    encoded_accumulator: A byte string representing an accumulator.
  
  Returns:
    The accumulator represented by the passed byte_string.
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code: def deserialize(self, encoded_accumulator):
      """Deserialize an accumulator received from 'serialize()'.
  
      This function deserializes an accumulator serialized by 'serialize()'.
  
      Args:
        encoded_accumulator: A byte string representing an accumulator.
  
      Returns:
        The accumulator represented by the passed byte_string.
      """
      pass
  
imports: import abc
  import collections
  import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import data_adapter
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.keras.utils import version_utils
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.trackable import base
label_desc: Use image node_id 7 for calling the Combiner obj's underlying member method code with example usage: obj.deserialize(encoded_accumulator) without return types