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 rsample(self, sample_shape=torch.Size(())):
      shape = self._extended_shape(sample_shape)
      #   X1 ~ Gamma(df1 / 2, 1 / df1), X2 ~ Gamma(df2 / 2, 1 / df2)
      #   Y = df2 * df1 * X1 / (df1 * df2 * X2) = X1 / X2 ~ F(df1, df2)
      X1 = self._gamma1.rsample(sample_shape).view(shape)
      X2 = self._gamma2.rsample(sample_shape).view(shape)
      tiny = torch.finfo(X2.dtype).tiny
      X2.clamp_(min=tiny)
      Y = X1 / X2
      Y.clamp_(min=tiny)
      return Y
  
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 6 for calling the FisherSnedecor obj's underlying member method code with example usage: obj.rsample(sample_shape) and returns: Y