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","."]
code: def _get_prediction_head_files(cls, load_dir, strict=True):
      load_dir = Path(load_dir)
      files = os.listdir(load_dir)
      config_files = [
          load_dir / f
          for f in files
          if "config.json" in f and "prediction_head" in f
      ]
      # sort them to get correct order in case of multiple prediction heads
      config_files.sort()
      return config_files
  
imports: import copy
  import json
  import logging
  import os
  from argparse import Namespace
  from pathlib import Path
  import multiprocessing
  import numpy
  import torch
  from torch import nn
  from farm.data_handler.processor import TextSimilarityProcessor
  from farm.data_handler.data_silo import DataSilo
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import PredictionHead, TextSimilarityHead
  from farm.modeling.tokenization import Tokenizer
  from farm.utils import MLFlowLogger, stack
label_desc: Use image node_id 7 for calling the BaseBiAdaptiveModel obj's underlying member method code with example usage: obj._get_prediction_head_files(cls, load_dir, strict) and returns: config_files