class_docstr: Implementation of ONNX Runtime for Inference of ONNX Models.
  
  Existing PyTorch based FARM AdaptiveModel can be converted to ONNX format using AdaptiveModel.convert_to_onnx().
  The conversion is currently only implemented for Question Answering Models.
  
  For inference, this class is compatible with the FARM Inferencer.
class_docstr_tok: ["Implementation","of","ONNX","Runtime","for","Inference","of","ONNX","Models",".","Existing","PyTorch","based","FARM","AdaptiveModel","can","be","converted","to","ONNX","format","using","AdaptiveModel.convert_to_onnx","(",")",".","The","conversion","is","currently","only","implemented","for","Question","Answering","Models",".","For","inference",",","this","class","is","compatible","with","the","FARM","Inferencer","."]
docstr: Stub to make ONNXAdaptiveModel compatible with the PyTorch AdaptiveModel.
code: def eval(self):
      """
      Stub to make ONNXAdaptiveModel compatible with the PyTorch AdaptiveModel.
      """
      return True
  
imports: import copy
  import json
  import logging
  import os
  from pathlib import Path
  import multiprocessing
  import numpy
  import torch
  from torch import nn
  from transformers import AutoConfig
  from transformers.convert_graph_to_onnx import convert, quantize
  from farm.data_handler.processor import Processor
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import PredictionHead, pick_single_fn
  from farm.utils import MLFlowLogger, stack
  import farm.conversion.transformers
label_desc: Use image node_id 4 for calling the ONNXAdaptiveModel obj's underlying member method code with example usage: obj.eval() and returns: True