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 log_prob(self, value):
      if self._validate_args:
          self._validate_sample(value)
      y = (value - self.loc) / self.scale
      Z = (
          self.scale.log()
          + 0.5 * self.df.log()
          + 0.5 * math.log(math.pi)
          + torch.lgamma(0.5 * self.df)
          - torch.lgamma(0.5 * (self.df + 1.0))
      )
      return (
          -0.5 * (self.df + 1.0) * torch.log1p(y**2.0 / self.df) - Z
      )
  
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 7 for calling the StudentT obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown