code: def random_sample(self):
          
          # TODO: support integer, category, and basic scipy.optimize constraints
          data = np.empty((1, self.dim))
          for col, (lower, upper) in enumerate(self._bounds):
              data.T[col] = self.random_state.uniform(lower, upper, size=1)
          return data.ravel()
docs: Creates random points within the bounds of the space.
  
          Returns
          ----------
          data: ndarray
              [num x dim] array points with dimensions corresponding to `self._keys`
  
          Example
          -------
          >>> target_func = lambda p1, p2: p1 + p2
          >>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
          >>> space = TargetSpace(target_func, pbounds, random_state=0)
          >>> space.random_points(1)
          array([[ 55.33253689,   0.54488318]])