class_docstr: Creates a Student's t-distribution parameterized by degree of
  freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale`.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = StudentT(torch.tensor([2.0]))
      >>> m.sample()  # Student's t-distributed with degrees of freedom=2
      tensor([ 0.1046])
  
  Args:
      df (float or Tensor): degrees of freedom
      loc (float or Tensor): mean of the distribution
      scale (float or Tensor): scale of the distribution
code: def rsample(self, sample_shape=torch.Size()):
      # NOTE: This does not agree with scipy implementation as much as other distributions.
      # (see https://github.com/fritzo/notebooks/blob/master/debug-student-t.ipynb). Using DoubleTensor
      # parameters seems to help.
  
      #   X ~ Normal(0, 1)
      #   Z ~ Chi2(df)
      #   Y = X / sqrt(Z / df) ~ StudentT(df)
      shape = self._extended_shape(sample_shape)
      X = _standard_normal(
          shape, dtype=self.df.dtype, device=self.df.device
      )
      Z = self._chi2.rsample(sample_shape)
      Y = X * torch.rsqrt(Z / self.df)
      return self.loc + self.scale * Y
  
file_path: pytorch/torch/distributions/studentT.py
imports: import math
  import torch
  from torch import inf, nan
  from torch.distributions import Chi2, constraints
  from torch.distributions.distribution import Distribution
  from torch.distributions.utils import _standard_normal, broadcast_all
label_desc: Use image node_id 6 for calling the StudentT obj's underlying member method code with example usage: obj.rsample(sample_shape) and returns: unknown