class_docstr: Calculates the cumulative mean.
  
  Description:
      Given a list of values, return the cumulative mean
      (or running mean). There is no set window, so the
      mean at each point is calculated over all prior values.
      `NaN` values will return `NaN`, but in the window of a
      cumulative caluclation, they're treated as 0.
  
  Examples:
      >>> cum_mean = CumMean()
      >>> cum_mean([1, 2, 3, 4, None, 5]).tolist()
      [1.0, 1.5, 2.0, 2.5, nan, 2.5]
class_docstr_tok: ["Calculates","the","cumulative","mean",".","Description",":","Given","a","list","of","values",",","return","the","cumulative","mean","(","or","running","mean",")",".","There","is","no","set","window",",","so","the","mean","at","each","point","is","calculated","over","all","prior","values",".","`","NaN","`","values","will","return","`","NaN","`",",","but","in","the","window","of","a","cumulative","caluclation",",","they","'re","treated","as","0",".","Examples",":",">",">",">","cum_mean","=","CumMean","(",")",">",">",">","cum_mean","(","[","1",",","2",",","3",",","4",",","None",",","5","]",")",".tolist","(",")","[","1.0",",","1.5",",","2.0",",","2.5",",","nan",",","2.5","]"]
code: def get_function(self):
      def cum_mean(values):
          return values.cumsum() / np.arange(1, len(values) + 1)
  
      return cum_mean
  
imports: import numpy
  from woodwork.column_schema import ColumnSchema
  from featuretools.primitives.base import TransformPrimitive
label_desc: Use image node_id 1 for calling the CumMean obj's underlying member method code with example usage: obj.get_function() and returns: cum_mean, unknown