class_docstr: A running variance computation.
  
  This is just an alias for `RunningCovariance`, with the `event_ndims` set to 0
  to compute variances.
  
  `RunningVariance` is meant to serve general streaming variance needs.
  For a specialized version that fits streaming over MCMC samples, see
  `VarianceReducer` in `tfp.experimental.mcmc`.
code: def __repr__(self):
      return (
          "RunningVariance(\n"
          f"    num_samples={self.num_samples!r},\n"
          f"    mean={self.mean!r},\n"
          f"    sum_squared_residuals={self.sum_squared_residuals!r})"
      )
  
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 RunningVariance obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str, self, str, self, str