code: def test_gradient_accumulation(
      autocast, scaler, nullcontext, mem_trace, mocker
  ):
      model = mocker.MagicMock(name="model")
      model().loss.__truediv__().detach.return_value = torch.tensor(1)
      mock_tensor = mocker.MagicMock(name="tensor")
      batch = {"input": mock_tensor}
      train_dataloader = [batch, batch, batch, batch, batch]
      eval_dataloader = None
      tokenizer = mocker.MagicMock()
      optimizer = mocker.MagicMock()
      lr_scheduler = mocker.MagicMock()
      gradient_accumulation_steps = 1
      train_config = mocker.MagicMock()
      train_config.enable_fsdp = False
      train_config.use_fp16 = False
      train_config.run_validation = False
      train_config.gradient_clipping = False
      train_config.save_metrics = False
  
      train(
          model,
          train_dataloader,
          eval_dataloader,
          tokenizer,
          optimizer,
          lr_scheduler,
          gradient_accumulation_steps,
          train_config,
      )
  
      assert optimizer.zero_grad.call_count == 5
      optimizer.zero_grad.reset_mock()
  
      assert nullcontext.call_count == 5
      nullcontext.reset_mock()
  
      assert autocast.call_count == 0
  
      gradient_accumulation_steps = 2
      train_config.use_fp16 = True
      train(
          model,
          train_dataloader,
          eval_dataloader,
          tokenizer,
          optimizer,
          lr_scheduler,
          gradient_accumulation_steps,
          train_config,
      )
      assert optimizer.zero_grad.call_count == 3
      assert nullcontext.call_count == 0
      assert autocast.call_count == 5
  
file_path: llama-recipes/tests/test_train_utils.py
imports: from unittest.mock import patch
  import pytest
  import torch
  import os
  import shutil
  from llama_recipes.utils.train_utils import train
label_desc: Use image node_id 2 for calling a global function with example usage: test_gradient_accumulation(autocast, scaler, nullcontext, mem_trace, mocker) without return types