optimization), but are interpreted as angles modulo 2 pi.
Example::
      >>> m = VonMises(torch.tensor([1.0]), torch.tensor([1.0]))
      >>> m.sample()  # von Mises distributed with loc=1 and concentration=1
      tensor([1.9777])
Parameters:
      * loc (torch.Tensor) -- an angle in radians.
  * **concentration** (*torch.Tensor*) -- concentration parameter

arg_constraints = {'concentration': GreaterThan(lower_bound=0.0), 'loc': Real()}
expand(batch_shape)
has_rsample = False
log_prob(value)
property mean
  The provided mean is the circular one.

property mode
sample(sample_shape=torch.Size([]))
  The sampling algorithm for the von Mises distribution is based
  on the following paper: Best, D. J., and Nicholas I. Fisher.
  "Efficient simulation of the von Mises distribution." Applied
  Statistics (1979): 152-157.

support = Real()
property variance
  The provided variance is the circular one.

Weibull