class_docstr: Base class for all MCMC `Reducer`s.
  
  This class defines the minimal requirements to implement a Markov chain Monte
  Carlo (MCMC) reducer. A reducer updates a streaming computation by reducing
  new samples to a summary statistic. `Reducer`s can be defined to return "side
  information" if desired, but they do not remember state. Hence, reducers
  should be seen as objects that hold metadata (i.e. shape and dtype of
  incoming samples) and all reducer method calls must be coupled with a state
  object, as first returned by the `initialize` method.
docstr: Finalizes target statistic calculation from the `final_state`.
  
  This is an identity function of the `final_state` by default. Subclasses
  can override it for streaming calculations whose running state is not the
  same as the desired result.
  
  Args:
    final_reducer_state: A `tuple`, `namedtuple` or `list` of `Tensor`s
      representing the final state of the reduced statistic.
  
  Returns:
    statistic: An estimate of the target statistic
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code: def finalize(self, final_reducer_state):
      """Finalizes target statistic calculation from the `final_state`.
  
      This is an identity function of the `final_state` by default. Subclasses
      can override it for streaming calculations whose running state is not the
      same as the desired result.
  
      Args:
        final_reducer_state: A `tuple`, `namedtuple` or `list` of `Tensor`s
          representing the final state of the reduced statistic.
  
      Returns:
        statistic: An estimate of the target statistic
      """
      return final_reducer_state
  
imports: import abc
  import six
label_desc: Use image node_id 3 for calling the Reducer obj's underlying member method code with example usage: obj.finalize(final_reducer_state) and returns: final_reducer_state