code: def get_mine_function(self, strategy):
      if strategy in [self.HARD, self.SEMIHARD]:
          mine_func = (
              self.get_min_per_row
              if self.distance.is_inverted
              else self.get_max_per_row
          )
      elif strategy == self.EASY:
          mine_func = (
              self.get_max_per_row
              if self.distance.is_inverted
              else self.get_min_per_row
          )
      else:
          raise NotImplementedError
  
      return mine_func
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/miners/batch_easy_hard_miner.py
imports: import torch
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_miner import BaseMiner
label_desc: Use image node_id 5 for calling the BatchEasyHardMiner obj's underlying member method code with example usage: obj.get_mine_function(strategy) and returns: mine_func