code: def _sample_indices_and_probabilities(
      self, n: int, uniform_ratio: float
  ) -> Tuple[List[int], List[float], float]:
      total_priority: float = self.priority_sums.sum()
      min_prob = self.priority_mins.min() / total_priority
      indices = []
      priorities = []
      if uniform_ratio > 0:
          # Mix uniform samples and prioritized samples
          n_uniform = np.random.binomial(n, uniform_ratio)
          un_indices, un_priorities = self.priority_sums.uniform_sample(
              n_uniform, remove=self.wait_priority_after_sampling
          )
          indices.extend(un_indices)
          priorities.extend(un_priorities)
          n -= n_uniform
          min_prob = (
              uniform_ratio / len(self) + (1 - uniform_ratio) * min_prob
          )
  
      pr_indices, pr_priorities = self.priority_sums.prioritized_sample(
          n, remove=self.wait_priority_after_sampling
      )
      indices.extend(pr_indices)
      priorities.extend(pr_priorities)
  
      probs = [
          uniform_ratio / len(self)
          + (1 - uniform_ratio) * pri / total_priority
          for pri in priorities
      ]
      return indices, probs, min_prob
  
imports: import collections
  from typing import Any, Callable, Deque, Generic, List, Optional, Sequence, Tuple, TypeVar
  import numpy
  from pfrl.utils.random import sample_n_k
label_desc: Use image node_id 5 for calling the PrioritizedBuffer obj's underlying member method code with example usage: obj._sample_indices_and_probabilities(n, uniform_ratio) and returns: indices,  probs,  min_prob