
logits (Tensor) -- unnormalized log probability for each
        event

arg_constraints: Dict[str, constraints.Constraint] = {'logits': IndependentConstraint(Real(), 1), 'probs': Simplex()}
expand(batch_shape, _instance=None)
has_rsample = True
property logits
property probs
support = Simplex()
property temperature
StudentT
class torch.distributions.studentT.StudentT(df, loc=0.0, scale=1.0, validate_args=None)
Bases: "Distribution"
Creates a Student's t-distribution parameterized by degree of
   freedom "df", mean "loc" and scale "scale".
Example:
  >>> m = StudentT(torch.tensor([2.0]))
  >>> m.sample()  # Student's t-distributed with degrees of freedom=2
  tensor([ 0.1046])

Parameters:
      * df (float or Tensor) -- degrees of freedom
  * **loc** (*float** or **Tensor*) -- mean of the distribution

  * **scale** (*float** or **Tensor*) -- scale of the distribution
