class_docstr: Determines the length of the longest subsequence above the mean.
  
  Description:
      Given a list of numbers, find the longest subsequence of numbers
      larger than the mean of the entire sequence. Return the length
      of the longest subsequence.
  
  Args:
      skipna (bool): If this is False and any value in x is `NaN`, then
          the result will be `NaN`. If True, `NaN` values are skipped.
          Default is True.
  
  Examples:
      >>> num_consecutive_greater_mean = NumConsecutiveGreaterMean()
      >>> num_consecutive_greater_mean([1, 2, 3, 4, 5, 6])
      3.0
  
      We can also control the way `NaN` values are handled.
  
      >>> num_consecutive_greater_mean = NumConsecutiveGreaterMean(skipna=False)
      >>> num_consecutive_greater_mean([1, 2, 3, 4, 5, 6, None])
      nan
code: def get_function(self):
      def num_consecutive_greater_mean(x):
          # check for NaN cases
          if x.isnull().all():
              return np.nan
          if not self.skipna and x.isnull().values.any():
              return np.nan
          x_mean = x.mean()
  
          # In some cases, the mean of x may be NaN
          #   (such as when x has both inf and -inf values)
          if np.isnan(x.mean()):
              return np.nan
  
          # Find indices of points at or below mean
          x = x.dropna().reset_index(drop=True)
          below_mean_indices = x[x <= x_mean].index.to_series()
  
          # If none of x is below the mean, return the length of x
          if below_mean_indices.empty:
              return len(x)
  
          # Pad index with start/end values, in case the longest
          #   sequence occurs at the beginning or end of x
          below_mean_indices[-1] = -1
          below_mean_indices[len(x)] = len(x)
          below_mean_indices = below_mean_indices.sort_index()
  
          # Calculate gaps between points below mean
          below_mean_indices_shifted = below_mean_indices.shift(1)
          diffs = below_mean_indices - below_mean_indices_shifted
  
          # Take biggest gap, and subtract 1 to get result
          max_gap = (diffs).max() - 1
          return max_gap
  
      return num_consecutive_greater_mean
  
imports: import numpy
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import IntegerNullable
  from featuretools.primitives.base import AggregationPrimitive
label_desc: Use image node_id 2 for calling the NumConsecutiveGreaterMean obj's underlying member method code with example usage: obj.get_function() and returns: num_consecutive_greater_mean, max_gap, np, np, np, len