dimension of "probs" indexes over categories. All other dimensions
   index over batches.
Note that "total_count" need not be specified if only "log_prob()"
   is called (see example below)
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
 1 along the last dimension. "logits" will return this normalized
 value.



"sample()" requires a single shared total_count for all
     parameters and samples.


"log_prob()" allows different total_count for each parameter
     and sample.


Example:
  >>> m = Multinomial(100, torch.tensor([ 1., 1., 1., 1.]))
  >>> x = m.sample()  # equal probability of 0, 1, 2, 3
