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 __init__(self, df1, df2, validate_args=None):
      self.df1 = df1
      self.df2 = df2
      self._gamma1 = Gamma(0.5 * self.df1, 1 / self.df1)
      self._gamma2 = Gamma(0.5 * self.df2, 1 / self.df2)
      super(FisherSnedecor, self).__init__(
          event_dim=0, validate_args=validate_args
      )
  
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 1 to create a new FisherSnedecor object from inherited base classes: Distribution with example: obj = FisherSnedecor(df1, df2, validate_args)