docstr: Args:
      model_path (`Union[str, os.PathLike]`):
          The path to the model ONNX Intermediate Representation (IR).
      execution_provider (:obj:`str`, `optional`):
          ONNX Runtime execution provider to use.
      compute_metrics (`Callable[[EvalPrediction], Dict]`, `optional`):
          The function that will be used to compute metrics at evaluation. Must take an `EvalPrediction` and
          return a dictionary string to metric values.
      label_names (`List[str]`, `optional`):
          The list of keys in your dictionary of inputs that correspond to the labels.
code: def __init__(
      self,
      model_path: Union[str, os.PathLike],
      execution_provider: Optional[str] = "CPUExecutionProvider",
      compute_metrics: Optional[
          Callable[[EvalPrediction], Dict]
      ] = None,
      label_names: Optional[List[str]] = None,
  ):
      """
      Args:
          model_path (`Union[str, os.PathLike]`):
              The path to the model ONNX Intermediate Representation (IR).
          execution_provider (:obj:`str`, `optional`):
              ONNX Runtime execution provider to use.
          compute_metrics (`Callable[[EvalPrediction], Dict]`, `optional`):
              The function that will be used to compute metrics at evaluation. Must take an `EvalPrediction` and
              return a dictionary string to metric values.
          label_names (`List[str]`, `optional`):
              The list of keys in your dictionary of inputs that correspond to the labels.
      """
      self.compute_metrics = compute_metrics
      self.label_names = (
          ["labels"] if label_names is None else label_names
      )
      self.session = InferenceSession(
          str(model_path), providers=[execution_provider]
      )
      self.onnx_input_names = {
          input_key.name: idx
          for idx, input_key in enumerate(self.session.get_inputs())
      }
  
file_path: optimum/optimum/onnxruntime/model.py
imports: import logging
  import os
  from typing import Callable, Dict, List, Optional, Union
  import numpy
  from datasets import Dataset
  from transformers import EvalPrediction
  from transformers.trainer_pt_utils import nested_concat
  from transformers.trainer_utils import EvalLoopOutput
  from onnxruntime import InferenceSession
label_desc: Use image node_id 1 to create a new ORTModel object  with example: obj = ORTModel(model_path, execution_provider, compute_metrics, label_names)