code: def __init__(
      self, loss, embedding_size, memory_size=1024, miner=None, **kwargs
  ):
      super().__init__(loss=loss, **kwargs)
      self.loss = loss
      self.miner = miner
      self.embedding_size = embedding_size
      self.memory_size = memory_size
      self.reset_queue()
      self.add_to_recordable_attributes(
          list_of_names=["embedding_size", "memory_size", "queue_idx"],
          is_stat=False,
      )
  
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 1 to create a new CrossBatchMemory object from inherited base classes: BaseLossWrapper, ModuleWithRecords with example: obj = CrossBatchMemory(loss, embedding_size, memory_size, miner)