code: def run_no_op(
      tmp_path: pathlib.Path,
      num_steps: int = 1,
      global_batch_size: int = 32,
      dataset_len: int = 64,
  ):
      checkpoint_dir = str(tmp_path.joinpath("checkpoint"))
  
      config = utils.load_config(
          utils.fixtures_path("pytorch_no_op/const.yaml")
      )
      hparams = config["hyperparameters"]
      hparams["dataset_len"] = dataset_len
      hparams["global_batch_size"] = global_batch_size
  
      exp_config = utils.make_default_exp_config(
          hparams,
          scheduling_unit=1,
          searcher_metric="validation_loss",
          checkpoint_dir=checkpoint_dir,
      )
      exp_config.update(config)
      exp_config["searcher"]["smaller_is_better"] = True
  
      example_path = utils.fixtures_path("pytorch_no_op/model_def.py")
      trial_class = utils.import_class_from_module(
          "NoopPyTorchTrial", example_path
      )
      trial_class._searcher_metric = "validation_error"
  
      f = io.StringIO()
  
      with contextlib.redirect_stdout(f):
          pytorch_utils.train_for_checkpoint(
              hparams=hparams,
              trial_class=trial_class,
              tmp_path=tmp_path,
              exp_config=exp_config,
              slots_per_trial=2,
              steps=num_steps,
          )
  
      return f.getvalue().split("\n")
  
imports: import contextlib
  import importlib
  import io
  import os
  import pathlib
  import sys
  import typing
  from unittest import mock
  import numpy
  import pytest
  import torch
  from _pytest import monkeypatch
  from torch.distributed import launcher
  import determined
  from determined import pytorch
  from tests.experiment import pytorch_utils, utils
  from tests.experiment.fixtures import pytorch_onevar_model
label_desc: Use image node_id 6 for calling a global function with example usage: run_no_op(tmp_path, num_steps, global_batch_size, dataset_len) and returns: f