class_docstr: Wrapper Class for converting PyTorch models to ONNX.
  
  As of torch v1.4.0, torch.onnx.export only support passing positional arguments to the forward pass of the model.
  However, the AdaptiveModel's forward takes keyword arguments. This class circumvents the issue by converting
  positional arguments to keyword arguments.
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code: def forward(self, *batch):
      return super().forward(
          input_ids=batch[0],
          padding_mask=batch[1],
          segment_ids=batch[2],
      )
  
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 2 for calling the ONNXWrapper obj's underlying member method code with example usage: obj.forward() and returns: super