code: def forward_step(data_iterator, model: GPTModel):
      """Forward training step.
  
      Args:
          data_iterator : Input data iterator
          model (GPTModel): The GPT Model
      """
      args = get_args()
      timers = get_timers()
  
      # Get the batch.
      timers("batch-generator", log_level=2).start()
      (
          tokens,
          labels,
          loss_mask,
          attention_mask,
          position_ids,
      ) = get_batch(data_iterator)
      timers("batch-generator").stop()
  
      output_tensor = model(
          tokens, position_ids, attention_mask, labels=labels
      )
  
      return output_tensor, partial(loss_func, loss_mask)
  
imports: import os
  import torch
  from torch import Tensor
  from functools import partial
  from typing import Union
  from megatron import get_args
  from megatron import print_rank_0
  from megatron import get_timers
  from megatron import get_tokenizer
  from megatron.core import mpu, tensor_parallel
  from megatron.core.enums import ModelType
  from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
  from megatron.core.datasets.gpt_dataset import GPTDatasetConfig
  from megatron.core.datasets.gpt_dataset import GPTDataset
  import megatron.model
  from megatron.core.models.gpt import GPTModel
  from megatron.training import pretrain
  from megatron.core.transformer.spec_utils import import_module
  from megatron.utils import get_batch_on_this_cp_rank, get_batch_on_this_tp_rank, average_losses_across_data_parallel_group
  from megatron.arguments import core_transformer_config_from_args
  from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
label_desc: Use image node_id 4 for calling a global function with example usage: forward_step(data_iterator, model) and returns: output_tensor,  partial