parameters: (self, low, high)
return_statement: return randint(low, high)
docstring: An array of *size* random integers >= ``low`` and < ``high``.
docstring_summary: An array of *size* random integers >= ``low`` and < ``high``.
docstring_tokens: An array of * size * random integers > = low and < high .
function: def _rvs(self, low, high):
          """An array of *size* random integers >= ``low`` and < ``high``."""
          if self._size is not None:
              # Numpy's RandomState.randint() doesn't broadcast its arguments.
              # Use `broadcast_to()` to extend the shapes of low and high
              # up to self._size.  Then we can use the numpy.vectorize'd
              # randint without needing to pass it a `size` argument.
              low = broadcast_to(low, self._size)
              high = broadcast_to(high, self._size)
          randint = np.vectorize(self._random_state.randint, otypes=[np.int_])
          return randint(low, high)
function_tokens: def _rvs ( self , low , high ) : if self . _size is not None : # Numpy's RandomState.randint() doesn't broadcast its arguments. # Use `broadcast_to()` to extend the shapes of low and high # up to self._size.  Then we can use the numpy.vectorize'd # randint without needing to pass it a `size` argument. low = broadcast_to ( low , self . _size ) high = broadcast_to ( high , self . _size ) randint = np . vectorize ( self . _random_state . randint , otypes = [ np . int_ ] ) return randint ( low , high )