class_docstr: Calculates the number of values that are above the mean.
  
  Args:
      skipna (bool): Determines if to use NA/null values. Defaults to
          True to skip NA/null.
  
  Examples:
      >>> count_above_mean = CountAboveMean()
      >>> count_above_mean([1, 2, 3, 4, 5])
      2
  
      The way NaNs are treated can be controlled.
  
      >>> count_above_mean_skipna = CountAboveMean(skipna=False)
      >>> count_above_mean_skipna([1, 2, 3, 4, 5, None])
      nan
code: def __init__(self, skipna=True):
      self.skipna = skipna
  
imports: import numpy
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import IntegerNullable
  from featuretools.primitives.base.aggregation_primitive_base import AggregationPrimitive
label_desc: Use image node_id 1 to create a new CountAboveMean object from inherited base classes: AggregationPrimitive with example: obj = CountAboveMean(skipna)