class_docstr: Running standard error of the mean using Welford's algorithm.
  
  Parameters
  ----------
  ddof
      Delta Degrees of Freedom. The divisor used in calculations is `n - ddof`, where `n` is the
      number of seen elements.
  
  Attributes
  ----------
  n : int
      Number of observations.
  
  Examples
  --------
  
  >>> from river import stats
  
  >>> X = [3, 5, 4, 7, 10, 12]
  
  >>> sem = stats.SEM()
  >>> for x in X:
  ...     sem.update(x)
  ...     print(sem.get())
  0.0
  1.0
  0.577350
  0.853912
  1.240967
  1.447219
  
  >>> from river import utils
  
  >>> X = [1, 4, 2, -4, -8, 0]
  
  >>> rolling_sem = utils.Rolling(stats.SEM(ddof=1), window_size=3)
  >>> for x in X:
  ...     rolling_sem.update(x)
  ...     print(rolling_sem.get())
  0.0
  1.5
  0.881917
  2.403700
  2.905932
  2.309401
  
  References
  ----------
  [^1]: [Wikipedia article on algorithms for calculating variance](https://www.wikiwand.com/en/Algorithms_for_calculating_variance#/Covariance)
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code: def get(self):
      try:
          return (super().get() / self.mean.n) ** 0.5
      except ZeroDivisionError:
          return None
  
imports: from __future__ import annotations
  from .None import var
label_desc: Use image node_id 1 for calling the SEM obj's underlying member method code with example usage: obj.get() and returns: unknown, None