code: def maybe_mine_embeddings(
      self, embeddings, labels, prev_indices_tuple, curr_miner_name
  ):
      if curr_miner_name in self.mining_funcs:
          curr_miner = self.mining_funcs[curr_miner_name]
          if isinstance(curr_miner, miners.HDCMiner):
              curr_miner.set_idx_externally(prev_indices_tuple, labels)
              curr_indices_tuple = curr_miner(embeddings, labels)
              curr_miner.reset_idx()
          else:
              curr_indices_tuple = curr_miner(embeddings, labels)
          return curr_indices_tuple
      return None
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/trainers/cascaded_embeddings.py
imports: from ..None import miners
  from ..utils import common_functions
  from .base_trainer import BaseTrainer
label_desc: Use image node_id 4 for calling the CascadedEmbeddings obj's underlying member method code with example usage: obj.maybe_mine_embeddings(embeddings, labels, prev_indices_tuple, curr_miner_name) and returns: None, curr_indices_tuple