interval "[low, high)".
Example:
  >>> m = Uniform(torch.tensor([0.0]), torch.tensor([5.0]))
  >>> m.sample()  # uniformly distributed in the range [0.0, 5.0)
  tensor([ 2.3418])

Parameters:
      * low (float or Tensor) -- lower range (inclusive).
  * **high** (*float** or **Tensor*) -- upper range (exclusive).

arg_constraints = {'high': Dependent(), 'low': Dependent()}
cdf(value)
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
icdf(value)
log_prob(value)
property mean
property mode
rsample(sample_shape=torch.Size([]))
property stddev
property support
property variance
VonMises
class torch.distributions.von_mises.VonMises(loc, concentration, validate_args=None)
Bases: "Distribution"
A circular von Mises distribution.
This implementation uses polar coordinates. The "loc" and "value"
   args can be any real number (to facilitate unconstrained