code: def test_train_ignore_torch_seed(
      raw_dataset_fp: str, random_seed: int
  ) -> None:
      """Test reproducibility of training API when an unrelated torch random operation is performed between the
      Ludwig operations.
  
      Args:
          raw_dataset_fp (str): file path for data to be used as part of this test
          random_seed(int): random seed integer to use for test
  
      Returns: None
      """
      # define Ludwig model
      model1 = LudwigModel(config=CONFIG, logging_level=logging.WARN)
      training_statistics1, preprocessed_data1, _ = model1.train(
          dataset=raw_dataset_fp,
          random_seed=random_seed,
          skip_save_progress=True,
          skip_save_processed_input=True,
      )
  
      # invoke torch random functions with unrelated seed to
      # see if it affects Ludwig reproducibility
      torch.manual_seed(random_seed + 5)
      torch.rand((5,))
  
      model2 = LudwigModel(config=CONFIG, logging_level=logging.WARN)
      training_statistics2, preprocessed_data2, _ = model2.train(
          dataset=raw_dataset_fp,
          random_seed=random_seed,
          skip_save_progress=True,
          skip_save_processed_input=True,
      )
  
      # confirm data splits are reproducible
      for i in range(3):
          for k in preprocessed_data1[i].dataset:
              # same seeds should result in same output
              assert np.all(
                  preprocessed_data1[i].dataset[k]
                  == preprocessed_data2[i].dataset[k]
              )
  
      # confirm reproducibility/non-reproducibility of results
      assert training_statistics1 == training_statistics2
  
file_path: ludwig/tests/integration_tests/test_reproducibility.py
imports: import logging
  import os
  import pathlib
  import random
  import numpy
  import pytest
  import torch
  from ludwig.api import LudwigModel
  from ludwig.data.dataset_synthesizer import cli_synthesize_dataset
label_desc: Use image node_id 5 for calling a global function with example usage: test_train_ignore_torch_seed(raw_dataset_fp, random_seed) without return types