class_docstr: This class is for testing directly via get_regression_trainer
  
  It mixes in `TrainerIntegrationCommon` which already has a lot of helper validation methods
  which we can re-use here.
  
  Important: this class' setup can only work with a single gpu because it runs within the current
  pytest worker. For multi-gpu tests use TestDeepSpeedWithLauncher.
  
  Note: if any of the tests of this class get run there will be at least one gpu occupied by them
  until this pytest worker exits. This is because the gpu memory allocated by the cuda-kernels
  won't be released until this pytest worker exits.
  
  This may appear as some run-away tests if you watch `nvidia-smi` while other tests that fork new
  processes are run. So there will be one or two "stale" processes reported in `nvidia-smi`. This
  is not a bug.
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code: def test_load_best_model(self, stage, dtype):
      # Test that forced deepspeed reinit doesn't break the model. the forced re-init after
      # loading the best model in Trainer is there to workaround this bug in Deepspeed
      # https://github.com/microsoft/DeepSpeed/issues/1612
      #
      # The test is derived from a repro script submitted in this Issue:
      # https://github.com/huggingface/transformers/issues/17114
      #
      # One additional feature of this test is that we use a non-AdamW optimizer to test that
      # deepspeed doesn't fallback to AdamW, which would prevent the optimizer states from loading
      # correctly
  
      from transformers import (
          T5ForConditionalGeneration,
          T5Tokenizer,
          Trainer,
      )  # noqa
  
      output_dir = (
          self.get_auto_remove_tmp_dir()
      )  # "./xxx", after=False, before=False)
  
      ds_config_dict = self.get_config_dict(stage)
      del ds_config_dict["optimizer"]  # will use HF Trainer optimizer
      del ds_config_dict["scheduler"]  # will use HF Trainer scheduler
      ds_config_dict[
          "zero_force_ds_cpu_optimizer"
      ] = False  # offload is not efficient w/o CPUAdam
      # must use this setting to get the reload path exercised
      ds_config_dict["zero_optimization"][
          "stage3_gather_16bit_weights_on_model_save"
      ] = True
  
      with mockenv_context(**self.dist_env_1_gpu):
          args_dict = {
              "per_device_train_batch_size": 1,
              "per_device_eval_batch_size": 1,
              "gradient_accumulation_steps": 1,
              "learning_rate": 1e-4,
              "num_train_epochs": 1,
              "do_train": True,
              "do_eval": True,
              "optim": "adafactor",
              "evaluation_strategy": "steps",
              "eval_steps": 1,
              "save_strategy": "steps",
              "save_steps": 1,
              "load_best_model_at_end": True,
              "max_steps": 1,
              "deepspeed": ds_config_dict,
              "report_to": "none",
          }
  
          training_args = TrainingArguments(output_dir, **args_dict)
          tokenizer = T5Tokenizer.from_pretrained(T5_TINY)
          model = T5ForConditionalGeneration.from_pretrained(T5_TINY)
  
          def _add_eos_to_examples(example):
              example[
                  "input_text"
              ] = f"question: {example['question']}  context: {example['context']}"
              example["target_text"] = (
                  example["answers"]["text"][0]
                  if len(example["answers"]["text"]) > 0
                  else ""
              )
              return example
  
          def _convert_to_features(example_batch):
              input_encodings = tokenizer.batch_encode_plus(
                  example_batch["input_text"],
                  pad_to_max_length=True,
                  max_length=512,
                  truncation=True,
              )
              target_encodings = tokenizer.batch_encode_plus(
                  example_batch["target_text"],
                  pad_to_max_length=True,
                  max_length=16,
                  truncation=True,
              )
  
              encodings = {
                  "input_ids": input_encodings["input_ids"],
                  "attention_mask": input_encodings["attention_mask"],
                  "labels": target_encodings["input_ids"],
              }
  
              return encodings
  
          def get_dataset():
              data_file = str(
                  self.tests_dir
                  / "fixtures/tests_samples/SQUAD/sample.json"
              )
              data_files = {"train": data_file, "validation": data_file}
              raw_datasets = datasets.load_dataset(
                  "json", data_files=data_files, field="data"
              )
              train_dataset = (
                  raw_datasets["train"]
                  .map(_add_eos_to_examples)
                  .map(_convert_to_features, batched=True)
              )
              valid_dataset = deepcopy(train_dataset)
              return train_dataset, valid_dataset
  
          train_dataset, eval_dataset = get_dataset()
  
          trainer = Trainer(
              model=model,
              tokenizer=tokenizer,
              args=training_args,
              train_dataset=train_dataset,
              eval_dataset=eval_dataset,
          )
          trainer.train()  # crash 1 was here
          trainer.evaluate()
  
imports: import dataclasses
  import io
  import itertools
  import json
  import os
  import unittest
  from copy import deepcopy
  from functools import partial
  import datasets
  from parameterized import parameterized
  import tests.trainer.test_trainer
  from tests.trainer.test_trainer import TrainerIntegrationCommon
  from transformers import AutoModel, TrainingArguments, is_torch_available, logging
  from transformers.integrations.deepspeed import HfDeepSpeedConfig, is_deepspeed_available, unset_hf_deepspeed_config
  from transformers.testing_utils import CaptureLogger, CaptureStd, CaptureStderr, LoggingLevel, TestCasePlus, backend_device_count, execute_subprocess_async, mockenv_context, require_deepspeed, require_optuna, require_torch_accelerator, require_torch_multi_accelerator, slow, torch_device
  from transformers.trainer_utils import get_last_checkpoint, set_seed
  from transformers.utils import SAFE_WEIGHTS_NAME, is_torch_bf16_available_on_device
label_desc: Use image node_id 17 for calling the TrainerIntegrationDeepSpeed obj's underlying member method code with example usage: obj.test_load_best_model(stage, dtype) and returns: example, encodings, train_dataset,  valid_dataset