NegativeBinomial
class torch.distributions.negative_binomial.NegativeBinomial(total_count, probs=None, logits=None, validate_args=None)
Bases: "Distribution"
Creates a Negative Binomial distribution, i.e. distribution of the
   number of successful independent and identical Bernoulli trials
   before "total_count" failures are achieved. The probability of
   success of each Bernoulli trial is "probs".
Parameters:
      * total_count (float or Tensor) -- non-negative number
        of negative Bernoulli trials to stop, although the
        distribution is still valid for real valued count
  * **probs** (*Tensor*) -- Event probabilities of success in the
    half open interval [0, 1)

  * **logits** (*Tensor*) -- Event log-odds for probabilities of
    success

arg_constraints = {'logits': Real(), 'probs': HalfOpenInterval(lower_bound=0.0, upper_bound=1.0), 'total_count': GreaterThanEq(lower_bound=0)}
expand(batch_shape, _instance=None)