code: def create_indices_tuple(
      self,
      embeddings,
      labels,
      E_mem,
      L_mem,
      input_indices_tuple,
      do_remove_self_comparisons,
  ):
      if self.miner:
          indices_tuple = self.miner(embeddings, labels, E_mem, L_mem)
      else:
          indices_tuple = lmu.get_all_pairs_indices(labels, L_mem)
  
      if do_remove_self_comparisons:
          indices_tuple = lmu.remove_self_comparisons(
              indices_tuple, self.curr_batch_idx, self.memory_size
          )
  
      if input_indices_tuple is not None:
          if len(input_indices_tuple) == 3 and len(indices_tuple) == 4:
              input_indices_tuple = lmu.convert_to_pairs(
                  input_indices_tuple, labels
              )
          elif (
              len(input_indices_tuple) == 4 and len(indices_tuple) == 3
          ):
              input_indices_tuple = lmu.convert_to_triplets(
                  input_indices_tuple, labels
              )
          indices_tuple = c_f.concatenate_indices_tuples(
              indices_tuple, input_indices_tuple
          )
  
      return indices_tuple
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/cross_batch_memory.py
imports: import torch
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from ..utils.module_with_records import ModuleWithRecords
  from .base_loss_wrapper import BaseLossWrapper
label_desc: Use image node_id 6 for calling the CrossBatchMemory obj's underlying member method code with example usage: obj.create_indices_tuple(embeddings, labels, E_mem, L_mem, input_indices_tuple, do_remove__comparisons) and returns: indices_tuple