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`.
docstr: Returns the variance accumulated so far.
  
  Args:
    ddof: Requested dynamic degrees of freedom for the variance calculation.
      For example, use `ddof=0` for population variance and `ddof=1` for
      sample variance. Defaults to the population variance.
  
  Returns:
    variance: An estimate of the variance.
docstr_tok: ["Returns","the","variance","accumulated","so","far",".","Args",":","ddof",":","Requested","dynamic","degrees","of","freedom","for","the","variance","calculation",".","For","example",",","use","`","ddof=0","`","for","population","variance","and","`","ddof=1","`","for","sample","variance",".","Defaults","to","the","population","variance",".","Returns",":","variance",":","An","estimate","of","the","variance","."]
code: def variance(self, ddof=0):
      """Returns the variance accumulated so far.
  
      Args:
        ddof: Requested dynamic degrees of freedom for the variance calculation.
          For example, use `ddof=0` for population variance and `ddof=1` for
          sample variance. Defaults to the population variance.
  
      Returns:
        variance: An estimate of the variance.
      """
      return self.covariance(ddof)
  
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 RunningVariance obj's underlying member method code with example usage: obj.variance(ddof) and returns: self