code: def get_next(self):
      featurized_samples = None
      try:
          if self.batch_size == 1:
              featurized_samples = {
                  key: [value]
                  for key, value in next(self._dataset_iter).items()
              }
          else:
              featurized_samples = defaultdict(list)
              for _ in range(self.batch_size):
                  sample = next(self._dataset_iter)
  
                  for name, value in sample.items():
                      featurized_samples[name] += [value]
  
      except StopIteration:
          pass
  
      if featurized_samples is not None and len(featurized_samples) > 0:
          return featurized_samples
      return None
  
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 2 for calling the ORTCalibrationDataReader obj's underlying member method code with example usage: obj.get_next() and returns: None, featurized_samples