class_docstr: Computes running central moments.
  
  `RunningCentralMoments` will compute arbitrary central moments in
  streaming fashion following the formula proposed by Philippe Pebay
  (2008) [1]. For reference, the formula we refer to is the incremental
  version of arbitrary moments (equation 2.9). Since the algorithm computes
  moments as a function of lower ones, even if not requested, all lower
  moments will be computed as well. The moments that are actually returned
  is specified by the `moment` parameter at initialization. Note, while
  any arbitrarily high central moment is theoretically supported,
  `RunningCentralMoments` cannot guarantee numerical stability for all
  moments.
  
  #### References
  [1]: Philippe Pebay. Formulas for Robust, One-Pass Parallel Computation of
        Covariances and Arbitrary-Order Statistical Moments. _Technical Report
        SAND2008-6212_, 2008.
        https://prod-ng.sandia.gov/techlib-noauth/access-control.cgi/2008/086212.pdf
code: def tree_unflatten(cls, metadata, tensors):
      return cls(*tensors, *metadata)
  
imports: import functools
  import math
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.mcmc import diagnostic
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling the RunningCentralMoments obj's underlying member method code with example usage: obj.tree_unflatten(cls, metadata, tensors) and returns: cls