code: def __init__(self, dataset: Dataset, batch_size: int = 1):
      if dataset is None:
          raise ValueError("Provided dataset is None.")
  
      if batch_size <= 0:
          raise ValueError(
              f"Provided batch_size should be >= 1 (got: {batch_size})."
          )
  
      self.dataset = dataset
      self.batch_size = batch_size
  
      self._dataset_iter = iter(self.dataset)
  
imports: import logging
  import os
  from collections import defaultdict
  from pathlib import Path
  from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union
  import onnx
  from datasets import Dataset, load_dataset
  from packaging.version import Version, parse
  from transformers import AutoConfig
  from onnxruntime import __version__
  from onnxruntime.quantization import CalibrationDataReader, QuantFormat, QuantizationMode, QuantType
  from onnxruntime.quantization.onnx_quantizer import ONNXQuantizer
  from onnxruntime.quantization.qdq_quantizer import QDQQuantizer
  from ..quantization_base import OptimumQuantizer
  from ..utils.save_utils import maybe_save_preprocessors
  from .None import ORTQuantizableOperator
  from .configuration import CalibrationConfig, ORTConfig, QuantizationConfig
  from .modeling_ort import ORTModel
  from .modeling_seq2seq import ORTModelForConditionalGeneration
  from .preprocessors import QuantizationPreprocessor
label_desc: Use image node_id 1 to create a new ORTCalibrationDataReader object from inherited base classes: CalibrationDataReader with example: obj = ORTCalibrationDataReader(dataset, batch_size)