class_docstr: Determines the number of values that are below the mean.
  
  Args:
      skipna (bool): Determines if to use NA/null values. Defaults to
          True to skip NA/null.
  
  Examples:
      >>> count_below_mean = CountBelowMean()
      >>> count_below_mean([1, 2, 3, 4, 10])
      3
  
      The way NaNs are treated can be controlled.
  
      >>> count_below_mean_skipna = CountBelowMean(skipna=False)
      >>> count_below_mean_skipna([1, 2, 3, 4, 5, None])
      nan
code: def get_function(self):
      def count_below_mean(x):
          mean = x.mean(skipna=self.skipna)
          if np.isnan(mean):
              return np.nan
          return len(x[x < mean])
  
      return count_below_mean
  
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 2 for calling the CountBelowMean obj's underlying member method code with example usage: obj.get_function() and returns: count_below_mean, len, np