class_docstr: Handles the ONNX Runtime quantization process for models shared on huggingface.co/models.
class_docstr_tok: ["Handles","the","ONNX","Runtime","quantization","process","for","models","shared","on","huggingface.co\/models","."]
code: def clean_calibration_dataset(self, dataset: Dataset) -> Dataset:
      model = onnx.load(self.onnx_model_path)
      model_inputs = {input.name for input in model.graph.input}
      ignored_columns = list(set(dataset.column_names) - model_inputs)
      return dataset.remove_columns(ignored_columns)
  
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 8 for calling the ORTQuantizer obj's underlying member method code with example usage: obj.clean_calibration_dataset(dataset) and returns: dataset