code: def __init__(
      self,
      tokenizer,
      dataset,
      seq_length=1024,
      num_of_sequences=1024,
      chars_per_token=3.6,
  ):
      self.tokenizer = tokenizer
      self.concat_token_id = tokenizer.bos_token_id
      self.dataset = dataset
      self.seq_length = seq_length
      self.input_characters = (
          seq_length * chars_per_token * num_of_sequences
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/codeparrot/scripts/validation_loss.py
imports: import logging
  import torch
  from datasets import load_dataset
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from accelerate import Accelerator
  from arguments import EvaluationArguments
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
label_desc: Use image node_id 1 to create a new ConstantLengthDataset object from inherited base classes: IterableDataset with example: obj = ConstantLengthDataset(tokenizer, dataset, seq_length, num_of_sequences, chars_per_token)