class_docstr: Creates a Fisher-Snedecor distribution parameterized by :attr:`df1` and :attr:`df2`.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
      >>> m.sample()  # Fisher-Snedecor-distributed with df1=1 and df2=2
      tensor([ 0.2453])
  
  Args:
      df1 (float or Tensor): degrees of freedom parameter 1
      df2 (float or Tensor): degrees of freedom parameter 2
code: def log_prob(self, value):
      if self._validate_args:
          self._validate_sample(value)
      ct1 = self.df1 * 0.5
      ct2 = self.df2 * 0.5
      ct3 = self.df1 / self.df2
      t1 = (ct1 + ct2).lgamma() - ct1.lgamma() - ct2.lgamma()
      t2 = ct1 * ct3.log() + (ct1 - 1) * torch.log(value)
      t3 = (ct1 + ct2) * torch.log1p(ct3 * value)
      return t1 + t2 - t3
  
file_path: pytorch/torch/distributions/fishersnedecor.py
imports: from numbers import Number
  import torch
  from torch import nan
  from torch.distributions import constraints
  from torch.distributions.distribution import Distribution
  from torch.distributions.gamma import Gamma
  from torch.distributions.utils import broadcast_all
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