class_docstr: Creates a multivariate Student's t-distribution parameterized by degree of
  freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale_tril`.
  
  :param ~torch.Tensor df: degrees of freedom
  :param ~torch.Tensor loc: mean of the distribution
  :param ~torch.Tensor scale_tril: scale of the distribution, which is
      a lower triangular matrix with positive diagonal entries
class_docstr_tok: ["Creates","a","multivariate","Student","'s","t-distribution","parameterized","by","degree","of","freedom",":","attr",":","`","df","`",",","mean",":","attr",":","`","loc","`","and","scale",":","attr",":","`","scale_tril","`",".",":","param","~torch.Tensor","df",":","degrees","of","freedom",":","param","~torch.Tensor","loc",":","mean","of","the","distribution",":","param","~torch.Tensor","scale_tril",":","scale","of","the","distribution",",","which","is","a","lower","triangular","matrix","with","positive","diagonal","entries"]
code: def expand(self, batch_shape, _instance=None):
      new = self._get_checked_instance(MultivariateStudentT, _instance)
      batch_shape = torch.Size(batch_shape)
      loc_shape = batch_shape + self.event_shape
      scale_shape = loc_shape + self.event_shape
      new.df = self.df.expand(batch_shape)
      new.loc = self.loc.expand(loc_shape)
      new._unbroadcasted_scale_tril = self._unbroadcasted_scale_tril
      if "scale_tril" in self.__dict__:
          new.scale_tril = self.scale_tril.expand(scale_shape)
      if "covariance_matrix" in self.__dict__:
          new.covariance_matrix = self.covariance_matrix.expand(
              scale_shape
          )
      if "precision_matrix" in self.__dict__:
          new.precision_matrix = self.precision_matrix.expand(
              scale_shape
          )
      new._chi2 = self._chi2.expand(batch_shape)
      super(MultivariateStudentT, new).__init__(
          batch_shape, self.event_shape, validate_args=False
      )
      new._validate_args = self._validate_args
      return new
  
file_path: pyro/pyro/distributions/multivariate_studentt.py
imports: import math
  import torch
  from torch.distributions import constraints
  from torch.distributions.utils import lazy_property
  from pyro.distributions.torch import Chi2
  from pyro.distributions.torch_distribution import TorchDistribution
  from pyro.distributions.util import broadcast_shape
label_desc: Use image node_id 6 for calling the MultivariateStudentT obj's underlying member method code with example usage: obj.expand(batch_shape, _instance) and returns: new