class_docstr: Computes a running mean.
  
  In computation, samples can be provided individually or in chunks. A
  "chunk" of size M implies incorporating M samples into a single expectation
  computation at once, which is more efficient than one by one.
  
  `RunningMean` is meant to serve general streaming expectations.
  For a specialized version that fits streaming over MCMC samples, see
  `ExpectationsReducer` in `tfp.experimental.mcmc`.
code: def tree_flatten(self):
      return (self.num_samples, self.mean), ()
  
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 2 for calling the RunningMean obj's underlying member method code with example usage: obj.tree_flatten() and returns: self,  self,  