code: def test_iterative_step_from_tensor(self, model_name, input_name):
      with tempfile.TemporaryDirectory() as tmp_dir:
          # initialize dataset
          if input_name == "tensor":
              dummy_dataset = self._init_tensor_dummy_dataset()
              inputs = {
                  "input_ids": dummy_dataset["input_ids"],
                  "attention_mask": dummy_dataset["attention_mask"],
                  "labels": dummy_dataset["labels"],
              }
          else:
              dummy_dataset = self._init_textual_dummy_dataset()
              inputs = {
                  "texts": dummy_dataset["texts"],
                  "texts_labels": dummy_dataset["texts_labels"],
              }
  
          if model_name == "gpt2":
              model = self.model
              tokenizer = self.tokenizer
          else:
              model = self.t5_model
              tokenizer = self.t5_tokenizer
  
          args = TrainingArguments(
              output_dir=tmp_dir,
              per_device_train_batch_size=2,
              max_steps=2,
          )
          iterative_trainer = IterativeSFTTrainer(
              model=model, args=args, tokenizer=tokenizer
          )
  
          iterative_trainer.step(**inputs)
  
          for param in iterative_trainer.model.parameters():
              assert param.grad is not None
  
file_path: trl/tests/test_iterative_sft_trainer.py
imports: import tempfile
  import unittest
  import torch
  from datasets import Dataset
  from parameterized import parameterized
  from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, TrainingArguments
  from trl import IterativeSFTTrainer
label_desc: Use image node_id 5 for calling the IterativeTrainerTester obj's underlying member method code with example usage: obj.test_iterative_step_from_tensor(model_name, input_name) without return types