code: def _uniform_sample_indices_and_probabilities(self, n):
      indices = list(sample_n_k(len(self.data), n))
      probabilities = [1 / len(self)] * len(indices)
      return indices, probabilities
  
imports: import collections
  import numpy
  from chainerrl.misc.random import sample_n_k
label_desc: Use image node_id 8 for calling the PrioritizedBuffer obj's underlying member method code with example usage: obj._uniform_sample_indices_and_probabilities(n) and returns: indices,  probabilities