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 __init__(self, df, loc, scale_tril, validate_args=None):
      dim = loc.size(-1)
      assert scale_tril.shape[-2:] == (dim, dim)
      if not isinstance(df, torch.Tensor):
          df = loc.new_tensor(df)
      batch_shape = torch.broadcast_shapes(
          df.shape, loc.shape[:-1], scale_tril.shape[:-2]
      )
      event_shape = torch.Size((dim,))
      self.df = df.expand(batch_shape)
      self.loc = loc.expand(batch_shape + event_shape)
      self._unbroadcasted_scale_tril = scale_tril
      self._chi2 = Chi2(self.df)
      super().__init__(
          batch_shape, event_shape, validate_args=validate_args
      )
  
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 1 to create a new MultivariateStudentT object from inherited base classes: TorchDistribution with example: obj = MultivariateStudentT(df, loc, scale_tril, validate_args)