Bases: "Distribution"
Creates a categorical distribution parameterized by either "probs"
   or "logits" (but not both).
Note:
 It is equivalent to the distribution that "torch.multinomial()"
 samples from.

Samples are integers from {0, \ldots, K-1} where K is
   "probs.size(-1)".
If probs is 1-dimensional with length-K, each element is the
   relative probability of sampling the class at that index.
If probs is N-dimensional, the first N-1 dimensions are treated
   as a batch of relative probability vectors.
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
 likewise be normalized so that the resulting probabilities sum to
