class_docstr: Create a FisherSnedecor distribution object, often known as F distribution.
  
  Parameters
  ----------
  df1 : Tensor or scalar
      degree of freedom parameter 1
  scale : Tensor or scalar
      degree of freedom parameter 2
class_docstr_tok: ["Create","a","FisherSnedecor","distribution","object",",","often","known","as","F","distribution",".","Parameters","--","--","--","--","--","df1",":","Tensor","or","scalar","degree","of","freedom","parameter","1","scale",":","Tensor","or","scalar","degree","of","freedom","parameter","2"]
code: def variance(self):
      # variance is only define for df2 > 4
      df2 = np.where(self.df2 <= 4, nan, self.df2)
      df1 = self.df1
      numerator = 2 * df2**2 * (df1 + df2 - 2)
      denominator = df1 * (df2 - 2) ** 2 * (df2 - 4)
      return numerator / denominator
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/fishersnedecor.py
imports: from numpy import nan
  from .distribution import Distribution
  from .gamma import Gamma
  from .constraint import Positive
  from .utils import gammaln
  from ....None import np
label_desc: Use image node_id 4 for calling the FisherSnedecor obj's underlying member method code with example usage: obj.variance() and returns: unknown