class_docstr: Creates a one-hot categorical distribution parameterized by :attr:`probs` or
  :attr:`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. :attr:`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. :attr:`logits`
            will return this normalized value.
  
  See also: :func:`torch.distributions.Categorical` for specifications of
  :attr:`probs` and :attr:`logits`.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = OneHotCategorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
      >>> m.sample()  # equal probability of 0, 1, 2, 3
      tensor([ 0.,  0.,  0.,  1.])
  
  Args:
      probs (Tensor): event probabilities
      logits (Tensor): event log probabilities (unnormalized)
code: def probs(self):
      return self._categorical.probs
  
imports: import torch
  from torch.distributions import constraints
  from torch.distributions.categorical import Categorical
  from torch.distributions.distribution import Distribution
label_desc: Use image node_id 5 for calling the OneHotCategorical obj's underlying member method code with example usage: obj.probs() and returns: self