code: def _calculate_router_loss(
      self, aux_loss: list = None, z_loss: list = None
  ):
      if aux_loss is None or z_loss is None:
          aux_loss, z_loss = MOE_MANAGER.get_loss()
      assert (
          len(aux_loss)
          == len(z_loss)
          == self.config.num_hidden_layers
          // self.config.moe_layer_interval
      )
      aux_loss = (
          self.config.router_aux_loss_factor
          * sum(aux_loss)
          / len(aux_loss)
      )
      z_loss = (
          self.config.router_z_loss_factor * sum(z_loss) / len(z_loss)
      )
      return aux_loss, z_loss
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.llama.modeling_llama import LlamaConfig, LlamaRMSNorm
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from colossalai.kernel.cuda_native.mha.flash_attn_2 import HAS_FLASH_ATTN
  from colossalai.kernel.triton.llama_act_combine_kernel import HAS_TRITON
  from colossalai.moe.layers import SparseMLP
  from colossalai.moe.manager import MOE_MANAGER
  from colossalai.moe.utils import get_activation, set_moe_args
label_desc: Use image node_id 11 for calling the OpenMoeForCausalLM obj's underlying member method code with example usage: obj._calculate_router_loss(aux_loss, z_loss) and returns: aux_loss,  z_loss