code: def loss_func(labels, output_tensor):
      logits = output_tensor.contiguous().float()
      loss = F.cross_entropy(logits, labels)
  
      outputs = torch.argmax(logits, -1)
      correct = (outputs == labels).float()
      accuracy = torch.mean(correct)
  
      averaged_loss = average_losses_across_data_parallel_group(
          [loss, accuracy]
      )
  
      return loss, {
          "loss": averaged_loss[0],
          "accuracy": averaged_loss[1],
      }
  
file_path: megatron-lm/pretrain_vision_classify.py
imports: import torch
  import torch.nn.functional
  from functools import partial
  from megatron import get_args, get_timers, print_rank_0
  from megatron.core.enums import ModelType
  from megatron.data.vit_dataset import build_train_valid_datasets
  from megatron.model.vision.classification import VitClassificationModel
  from megatron.model.vision.classification import MitClassificationModel
  from megatron.training import pretrain
  from megatron.utils import average_losses_across_data_parallel_group
  from megatron.arguments import core_transformer_config_from_args
label_desc: Use image node_id 3 for calling a global function with example usage: loss_func(labels, output_tensor) and returns: loss,  dict