class_docstr: Running mean.
  
  Attributes
  ----------
  n : float
      The current sum of weights. If each passed weight was 1, then this is equal to the number
      of seen observations.
  
  Examples
  --------
  
  >>> from river import stats
  
  >>> X = [-5, -3, -1, 1, 3, 5]
  >>> mean = stats.Mean()
  >>> for x in X:
  ...     mean.update(x)
  ...     print(mean.get())
  -5.0
  -4.0
  -3.0
  -2.0
  -1.0
  0.0
  
  You can calculate a rolling average by wrapping a `utils.Rolling` around:
  
  >>> from river import utils
  
  >>> X = [1, 2, 3, 4, 5, 6]
  >>> rmean = utils.Rolling(stats.Mean(), window_size=2)
  
  >>> for x in X:
  ...     rmean.update(x)
  ...     print(rmean.get())
  1.0
  1.5
  2.5
  3.5
  4.5
  5.5
  
  References
  ----------
  [^1]: [West, D. H. D. (1979). Updating mean and variance estimates: An improved method. Communications of the ACM, 22(9), 532-535.](https://dl.acm.org/doi/10.1145/359146.359153)
  [^2]: [Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42.](https://fanf2.user.srcf.net/hermes/doc/antiforgery/stats.pdf)
  [^3]: [Chan, T.F., Golub, G.H. and LeVeque, R.J., 1983. Algorithms for computing the sample variance: Analysis and recommendations. The American Statistician, 37(3), pp.242-247.](https://amstat.tandfonline.com/doi/abs/10.1080/00031305.1983.10483115)
code: def get(self):
      return self._mean
  
imports: from __future__ import annotations
  import copy
  import numpy
  from river import stats
label_desc: Use image node_id 5 for calling the Mean obj's underlying member method code with example usage: obj.get() and returns: self