code: def test_gradient_accumulation(
      gradient_accumulation_steps: int, tmpdir
  ):
      input_features = [text_feature()]
      output_features = [
          category_feature(
              decoder={"vocab_size": 2}, reduce_input="sum"
          )
      ]
  
      csv_filename = os.path.join(tmpdir, "training.csv")
      data_csv = generate_data(
          input_features, output_features, csv_filename, num_examples=64
      )
      val_csv = shutil.copyfile(
          data_csv, os.path.join(tmpdir, "validation.csv")
      )
      test_csv = shutil.copyfile(
          data_csv, os.path.join(tmpdir, "test.csv")
      )
  
      trainer = {
          EPOCHS: 2,
          BATCH_SIZE: 8,
          "gradient_accumulation_steps": gradient_accumulation_steps,
      }
  
      config = {
          INPUT_FEATURES: input_features,
          OUTPUT_FEATURES: output_features,
          "combiner": {"type": "concat", "output_size": 14},
          TRAINER: trainer,
      }
  
      # Just test that training completes without error.
      # TODO(travis): We may want to expand upon this in the future to include some checks on model
      # convergence like gradient magnitudes, etc. Should also add distributed tests.
      model = LudwigModel(
          config, backend=LocalTestBackend(), logging_level=logging.INFO
      )
      model.train(
          training_set=data_csv,
          validation_set=val_csv,
          test_set=test_csv,
          output_directory=tmpdir,
      )
  
imports: import logging
  import os
  import shutil
  from unittest import mock
  import numpy
  import pandas
  import pytest
  import torch
  from packaging.version import parse
  from ludwig.api import LudwigModel
  from ludwig.callbacks import Callback
  from ludwig.constants import BATCH_SIZE, EFFECTIVE_BATCH_SIZE, EPOCHS, EVAL_BATCH_SIZE, INPUT_FEATURES, MAX_BATCH_SIZE_DATASET_FRACTION, OUTPUT_FEATURES, TRAINER
  from ludwig.distributed import init_dist_strategy
  from tests.integration_tests.utils import binary_feature, category_feature, generate_data, LocalTestBackend, number_feature, RAY_BACKEND_CONFIG, sequence_feature, text_feature, vector_feature
label_desc: Use image node_id 8 for calling a global function with example usage: test_gradient_accumulation(gradient_accumulation_steps, tmpdir) without return types