"df". This is exactly equivalent to "Gamma(alpha=0.5*df, beta=0.5)"
Example:
  >>> m = Chi2(torch.tensor([1.0]))
  >>> m.sample()  # Chi2 distributed with shape df=1
  tensor([ 0.1046])

Parameters:
      df (float or Tensor) -- shape parameter of the
      distribution
arg_constraints = {'df': GreaterThan(lower_bound=0.0)}
property df
expand(batch_shape, _instance=None)
ContinuousBernoulli
class torch.distributions.continuous_bernoulli.ContinuousBernoulli(probs=None, logits=None, lims=(0.499, 0.501), validate_args=None)
Bases: "ExponentialFamily"
Creates a continuous Bernoulli distribution parameterized by
   "probs" or "logits" (but not both).
The distribution is supported in [0, 1] and parameterized by
   'probs' (in (0,1)) or 'logits' (real-valued). Note that, unlike the
   Bernoulli, 'probs' does not correspond to a probability and
   'logits' does not correspond to log-odds, but the same names are