cdf(value)
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
icdf(value)
log_prob(value)
property mean
property mode
rsample(sample_shape=torch.Size([]))
sample(sample_shape=torch.Size([]))
property stddev
support = Real()
property variance
OneHotCategorical
class torch.distributions.one_hot_categorical.OneHotCategorical(probs=None, logits=None, validate_args=None)
Bases: "Distribution"
Creates a one-hot categorical distribution parameterized by "probs"
   or "logits".
Samples are one-hot coded vectors of size "probs.size(-1)".
Note:
 The *probs* argument must be non-negative, finite and have a non-
 zero sum, and it will be normalized to sum to 1 along the last
 dimension. "probs" will return this normalized value. The
 *logits* argument will be interpreted as unnormalized log
 probabilities and can therefore be any real number. It will
