row.
Note:
 When drawn without replacement, "num_samples" must be lower than
 number of non-zero elements in "input" (or the min number of non-
 zero elements in each row of "input" if it is a matrix).

Parameters:
      * input (Tensor) -- the input tensor containing
        probabilities
  * **num_samples** (*int*) -- number of samples to draw

  * **replacement** (*bool**, **optional*) -- whether to draw with
    replacement or not

Keyword Arguments:
      * generator ("torch.Generator", optional) -- a pseudorandom
        number generator for sampling
  * **out** (*Tensor**, **optional*) -- the output tensor.

Example:
  >>> weights = torch.tensor([0, 10, 3, 0], dtype=torch.float) # create a tensor of weights
  >>> torch.multinomial(weights, 2)
  tensor([1, 2])
  >>> torch.multinomial(weights, 4) # ERROR!
  RuntimeError: invalid argument 2: invalid multinomial distribution (with replacement=False,
