code: def _loss_func(self, loss_mask, output_tensor):
      losses = output_tensor.float()
      loss_mask = loss_mask.view(-1).float()
      loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
  
      # Reduce loss for logging.
      averaged_loss = average_losses_across_data_parallel_group([loss])
  
      return loss, {"lm loss": averaged_loss[0]}
  
imports: from functools import partial
  from typing import List
  import time
  import torch
  import unittest
  from apex.transformer._ucc_util import HAS_UCC
  from apex.transformer import parallel_state
  from apex.transformer.enums import ModelType
  from apex.transformer.tensor_parallel import model_parallel_cuda_manual_seed
  from apex.transformer.pipeline_parallel.utils import average_losses_across_data_parallel_group, unwrap_model, setup_microbatch_calculator, get_ltor_masks_and_position_ids
  from apex.transformer.pipeline_parallel.schedules.common import _get_params_for_weight_decay_optimization, build_model
  from apex.transformer.pipeline_parallel.schedules.fwd_bwd_pipelining_without_interleaving import forward_backward_pipelining_without_interleaving
  from apex.transformer.testing.standalone_gpt import gpt_model_provider
  from apex.transformer.testing import global_vars
  from apex.transformer.testing.distributed_test_base import UccDistributedTestBase, NcclDistributedTestBase
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_device_type import instantiate_device_type_tests
label_desc: Use image node_id 4 for calling the GptTestBase obj's underlying member method code with example usage: obj._loss_func(loss_mask, output_tensor) and returns: loss,  dict