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
docstr: Initialize an empty `RunningCentralMoments`.
  
  Args:
    example: A `Tensor`.  The `RunningCentralMoments` will accept
      samples of the same dtype and broadcast-compatible shape as
      the example.
    moment: Integer or iterable of integers that represent the
      desired moments to return.
  
  Returns:
    state: `RunningCentralMoments` representing a stream of no
      inputs.  Note that by convention, the supplied example is used
      only for initialization, but not counted as a sample.
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code: def from_example(cls, example, moment):
      """Initialize an empty `RunningCentralMoments`.
  
      Args:
        example: A `Tensor`.  The `RunningCentralMoments` will accept
          samples of the same dtype and broadcast-compatible shape as
          the example.
        moment: Integer or iterable of integers that represent the
          desired moments to return.
  
      Returns:
        state: `RunningCentralMoments` representing a stream of no
          inputs.  Note that by convention, the supplied example is used
          only for initialization, but not counted as a sample.
      """
      return cls.from_shape(ps.shape(example), moment, example.dtype)
  
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 5 for calling the RunningCentralMoments obj's underlying member method code with example usage: obj.from_example(cls, example, moment) and returns: cls