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 log_prob(self, value):
      if self._validate_args:
          self._validate_samples(value)
      lgamma = gammaln()
      log = np.log
      ct1 = self.df1 / 2
      ct2 = self.df2 / 2
      ct3 = self.df1 / self.df2
      t1 = (
          lgamma(ct1 + ct2) - lgamma(ct1) - lgamma(ct2)
      )  # Beta(df1/2, df2/2)
      t2 = log(ct3) * ct1 + (ct1 - 1) * log(value)
      t3 = (ct1 + ct2) * log(ct3 * value + 1)
      return t1 + t2 - t3
  
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 7 for calling the FisherSnedecor obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown