class_docstr: Base Class for implementing AdaptiveModel with frameworks like PyTorch and ONNX.
class_docstr_tok: ["Base","Class","for","implementing","AdaptiveModel","with","frameworks","like","PyTorch","and","ONNX","."]
docstr: This automatically keeps track of all available subclasses.
  Enables generic load() for all specific AdaptiveModel implementation.
code: def __init_subclass__(cls, **kwargs):
      """This automatically keeps track of all available subclasses.
      Enables generic load() for all specific AdaptiveModel implementation.
      """
      super().__init_subclass__(**kwargs)
      cls.subclasses[cls.__name__] = cls
  
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 1 for calling the BaseAdaptiveModel obj's underlying member method code with example usage: obj.__init_subclass__(cls) without return types