tensor([ 21.,  24.,  30.,  25.])
  >>> Multinomial(probs=torch.tensor([1., 1., 1., 1.])).log_prob(x)
  tensor([-4.1338])

Parameters:
      * total_count (int) -- number of trials
  * **probs** (*Tensor*) -- event probabilities

  * **logits** (*Tensor*) -- event log probabilities
    (unnormalized)

arg_constraints = {'logits': IndependentConstraint(Real(), 1), 'probs': Simplex()}
entropy()
expand(batch_shape, _instance=None)
log_prob(value)
property logits
property mean
property param_shape
property probs
sample(sample_shape=torch.Size([]))
property support
total_count: int
property variance
MultivariateNormal
class torch.distributions.multivariate_normal.MultivariateNormal(loc, covariance_matrix=None, precision_matrix=None, scale_tril=None, validate_args=None)
Bases: "Distribution"
Creates a multivariate normal (also called Gaussian) distribution