class_docstr: Computes the exponentially weighted moving average for a series of numbers
  
  Description:
      Returns the exponentially weighted moving average for a series of
      numbers. Exactly one of center of mass (com), span, half-life, and
      alpha must be provided. Missing values can be ignored when calculating
      weights by setting 'ignore_na' to True.
  
  Args:
      com (float): Specify decay in terms of center of mass for com >= 0.
          Default is None.
  
      span (float): Specify decay in terms of span for span >= 1.
          Default is None.
  
      halflife (float): Specify decay in terms of half-life for halflife > 0.
          Default is None.
  
      alpha (float): Specify smoothing factor alpha directly. Alpha should be
          greater than 0 and less than or equal to 1. Default is None.
  
      ignore_na (bool): Ignore missing values when calculating weights.
          Default is False.
  
  Examples:
      >>> exponential_weighted_average = ExponentialWeightedAverage(com=0.5)
      >>> exponential_weighted_average([1, 2, 3, 4]).tolist()
      [1.0, 1.75, 2.615384615384615, 3.55]
  
      Missing values can be ignored
      >>> ewma_ignorena = ExponentialWeightedAverage(com=0.5, ignore_na=True)
      >>> ewma_ignorena([1, 2, 3, None, 4]).tolist()
      [1.0, 1.75, 2.615384615384615, 2.615384615384615, 3.55]
code: def get_function(self):
      def exponential_weighted_average(x):
          return x.ewm(
              com=self.com,
              span=self.span,
              halflife=self.halflife,
              alpha=self.alpha,
              ignore_na=self.ignore_na,
          ).mean()
  
      return exponential_weighted_average
  
imports: from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Double
  from featuretools.primitives.base import TransformPrimitive
label_desc: Use image node_id 2 for calling the ExponentialWeightedAverage obj's underlying member method code with example usage: obj.get_function() and returns: exponential_weighted_average, x