code: def examples(self):
          
          # Always return the same numbers
          rand_state = np.random.get_state()
          np.random.seed(1337)
  
          if is_numeric_dtype(self.dtype):
              yield ('Linearly spaced samples', self.element(
                  np.linspace(0, 1, self.size).reshape(self.shape)))
              yield ('Normally distributed noise',
                     self.element(np.random.standard_normal(self.shape)))
  
          if self.is_real:
              yield ('Uniformly distributed noise',
                     self.element(np.random.uniform(size=self.shape)))
          elif self.is_complex:
              yield ('Uniformly distributed noise',
                     self.element(np.random.uniform(size=self.shape) +
                                  np.random.uniform(size=self.shape) * 1j))
          else:
              # TODO: return something that always works, like zeros or ones?
              raise NotImplementedError('no examples available for non-numeric'
                                        'data type')
  
          np.random.set_state(rand_state)
docs: Return example random vectors.