class_docstr: Determines the average count across all unique value.
  
  Args:
      skipna (bool): Determines if to use NA/null values.
          Defaults to True to skip NA/null.
  
  Examples:
      Determine the average count values for all unique items
      in the input
      >>> input = [1, 1, 2, 2, 3, 4, 5, 6, 7, 8]
      >>> avg_count_per_unique = AverageCountPerUnique()
      >>> avg_count_per_unique(input)
      1.25
  
      Determine the average count values for all unique items
      in the input with nan values ignored
      >>> input = [1, 1, 2, 2, 3, 4, 5, None, 6, 7, 8]
      >>> avg_count_per_unique = AverageCountPerUnique()
      >>> avg_count_per_unique(input)
      1.25
  
      Determine the average count values for all unique items
      in the input with nan values included
      >>> input = [1, 2, 2, 3, 4, 5, None, 6, 7, 8, 9]
      >>> avg_count_per_unique_skipna_false = AverageCountPerUnique(skipna=False)
      >>> avg_count_per_unique_skipna_false(input)
      1.1
code: def get_function(self):
      def average_count_per_unique(x):
          return x.value_counts(
              dropna=self.skipna,
          ).mean(skipna=self.skipna)
  
      return average_count_per_unique
  
imports: from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Double
  from featuretools.primitives.base import AggregationPrimitive
label_desc: Use image node_id 2 for calling the AverageCountPerUnique obj's underlying member method code with example usage: obj.get_function() and returns: average_count_per_unique, x