code: def train_step(
      strategy: Strategy,
      actor: GPTActor,
      actor_optim: HybridAdam,
      batch_size: int = 8,
  ):
      data = get_data(batch_size)
      action_mask = torch.ones_like(
          data["attention_mask"], dtype=torch.bool
      )
      actor_logits = actor(data["input_ids"], data["attention_mask"])[
          "logits"
      ]
      action_log_probs = calc_action_log_probs(
          actor_logits, data["input_ids"], action_mask.size(1)
      )
      loss = action_log_probs.sum()
      strategy.backward(loss, actor, actor_optim)
      strategy.optimizer_step(actor_optim)
  
file_path: colossalai/applications/Chat/tests/test_checkpoint.py
imports: import os
  import tempfile
  from contextlib import nullcontext
  import pytest
  import torch
  import torch.distributed
  from coati.models.gpt import GPTActor
  from coati.models.utils import calc_action_log_probs
  from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy, Strategy
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
  from colossalai.nn.optimizer import HybridAdam
  from colossalai.testing import rerun_if_address_is_in_use, spawn
label_desc: Use image node_id 2 for calling a global function with example usage: train_step(strategy, actor, actor_optim, batch_size) without return types