tensor([ 0.1046])
Parameters:
      * concentration (float or Tensor) -- shape parameter
        of the distribution (often referred to as alpha)
  * **rate** (*float** or **Tensor*) -- rate = 1 / scale of the
    distribution (often referred to as beta)

arg_constraints = {'concentration': GreaterThan(lower_bound=0.0), 'rate': GreaterThan(lower_bound=0.0)}
cdf(value)
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
log_prob(value)
property mean
property mode
rsample(sample_shape=torch.Size([]))
support = GreaterThanEq(lower_bound=0.0)
property variance
Geometric
class torch.distributions.geometric.Geometric(probs=None, logits=None, validate_args=None)
Bases: "Distribution"
Creates a Geometric distribution parameterized by "probs", where
   "probs" is the probability of success of Bernoulli trials. It
   represents the probability that in k + 1 Bernoulli trials, the