code: def create_dataloader(args):
      ds_kwargs = {"streaming": True}
      valid_data = load_dataset(
          args.dataset_name, split="train", **ds_kwargs
      )
      valid_dataset = ConstantLengthDataset(
          tokenizer, valid_data, seq_length=args.seq_length
      )
      eval_dataloader = DataLoader(
          valid_dataset, batch_size=args.batch_size
      )
      return eval_dataloader
  
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 for calling a global function with example usage: create_dataloader(args) and returns: eval_dataloader