code: def get_all_indices(self, n_samples=None, max_samples=None,
                          random_state=None):
          
          if self._indices_state is None and random_state is None:
              raise ValueError('The program has not been evaluated for fitness '
                               'yet, indices not available.')
  
          if n_samples is not None and self._n_samples is None:
              self._n_samples = n_samples
          if max_samples is not None and self._max_samples is None:
              self._max_samples = max_samples
          if random_state is not None and self._indices_state is None:
              self._indices_state = random_state.get_state()
  
          indices_state = check_random_state(None)
          indices_state.set_state(self._indices_state)
  
          not_indices = sample_without_replacement(
              self._n_samples,
              self._n_samples - self._max_samples,
              random_state=indices_state)
          sample_counts = np.bincount(not_indices, minlength=self._n_samples)
          indices = np.where(sample_counts == 0)[0]
  
          return indices, not_indices
docs: Get the indices on which to evaluate the fitness of a program.
  
          Parameters
          ----------
          n_samples : int
              The number of samples.
  
          max_samples : int
              The maximum number of samples to use.
  
          random_state : RandomState instance
              The random number generator.
  
          Returns
          -------
          indices : array-like, shape = [n_samples]
              The in-sample indices.
  
          not_indices : array-like, shape = [n_samples]
              The out-of-sample indices.