code: def test_constant_length_dataset(self):
      formatted_dataset = ConstantLengthDataset(
          self.tokenizer,
          self.dummy_dataset,
          dataset_text_field=None,
          formatting_func=formatting_prompts_func,
      )
  
      self.assertTrue(len(formatted_dataset) == len(self.dummy_dataset))
      self.assertTrue(len(formatted_dataset) > 0)
  
      for example in formatted_dataset:
          self.assertTrue("input_ids" in example)
          self.assertTrue("labels" in example)
  
          self.assertTrue(
              len(example["input_ids"]) == formatted_dataset.seq_length
          )
          self.assertTrue(
              len(example["labels"]) == formatted_dataset.seq_length
          )
  
          decoded_text = self.tokenizer.decode(example["input_ids"])
          self.assertTrue(
              ("Question" in decoded_text)
              and ("Answer" in decoded_text)
          )
  
imports: import copy
  import os
  import tempfile
  import unittest
  import numpy
  import torch
  from datasets import Dataset
  from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
  from trl import SFTTrainer
  from trl.import_utils import is_peft_available
  from trl.trainer import ConstantLengthDataset, DataCollatorForCompletionOnlyLM
  from .testing_utils import require_peft
label_desc: Use image node_id 2 for calling the SFTTrainerTester obj's underlying member method code with example usage: obj.test_constant_length_dataset() without return types