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 __init__(self, skipna=True):
      self.skipna = skipna
  
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 1 to create a new AverageCountPerUnique object from inherited base classes: AggregationPrimitive with example: obj = AverageCountPerUnique(skipna)