class_docstr: Calculates the kurtosis for a list of numbers
  
  Args:
      fisher (bool): Optional. If True, Fisher's definition is used
          (normal ==> 0.0). If False, Pearson's definition is used
          (normal ==> 3.0). Default is True.
      bias (bool): Optional. If False, then the calculations are
          corrected for statistical bias. Default is True.
      nan_policy (str): Optional. Defines how to handle when
          input contains Nan. Possible values include
          `['propagate', 'raise', 'omit']`. 'propagate'
          returns Nan, 'raise' throws an error, 'omit'
          performs the calculations ignoring Nan values.
          Default is 'propagate'.
  
  Examples:
      >>> kurtosis = Kurtosis()
      >>> kurtosis([1, 2, 3, 4, 5])
      -1.3
  
      You can use Pearson's definition by setting the 'fisher' argument to False
  
      >>> kurtosis_fisher = Kurtosis(fisher=False)
      >>> kurtosis_fisher([1, 2, 3, 4, 5])
      1.7
  
      You can correct for statistical bias by setting the 'bias' argument to False
  
      >>> kurtosis_bias = Kurtosis(bias=False)
      >>> kurtosis_bias([1, 2, 3, 4, 5])
      -1.2000000000000004
  
      You can specifiy how to handle NaN values in the input with the 'nan_policy'
      argument
  
      >>> kurtosis_nan_policy = Kurtosis(nan_policy='omit')
      >>> kurtosis_nan_policy([1, 2, None, 3, 4, 5])
      -1.3
code: def __init__(self, fisher=True, bias=True, nan_policy="propagate"):
      if nan_policy not in ["propagate", "raise", "omit"]:
          raise ValueError("Invalid nan_policy")
      self.fisher = fisher
      self.bias = bias
      self.nan_policy = nan_policy
  
imports: from scipy.stats import kurtosis
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Double, Integer
  from featuretools.primitives.base import AggregationPrimitive
label_desc: Use image node_id 1 to create a new Kurtosis object from inherited base classes: AggregationPrimitive with example: obj = Kurtosis(fisher, bias, nan_policy)