first k trials failed, before seeing a success.
Samples are non-negative integers [0, \inf).
Example:
  >>> m = Geometric(torch.tensor([0.3]))
  >>> m.sample()  # underlying Bernoulli has 30% chance 1; 70% chance 0
  tensor([ 2.])

Parameters:
      * probs (Number, Tensor) -- the probability of
        sampling 1. Must be in range (0, 1]
  * **logits** (*Number**, **Tensor*) -- the log-odds of sampling
    *1*.

arg_constraints = {'logits': Real(), 'probs': Interval(lower_bound=0.0, upper_bound=1.0)}
entropy()
expand(batch_shape, _instance=None)
log_prob(value)
property logits
property mean
property mode
property probs
sample(sample_shape=torch.Size([]))
support = IntegerGreaterThan(lower_bound=0)
property variance
Gumbel
class torch.distributions.gumbel.Gumbel(loc, scale, validate_args=None)
Bases: "TransformedDistribution"
Samples from a Gumbel Distribution.
Examples: