code: def _extract_configuration(job: TrainingJob) -> dict[str, Any]:
      model = job.hyperparameters["model"]
  
      hyperparameters = {}
      if "context_length_multiple" in job.hyperparameters:
          hyperparameters[
              "context_length_multiple"
          ] = job.hyperparameters["context_length_multiple"]
      for key, value in job.hyperparameters.items():
          if key.startswith(f"{model}_"):
              hyperparameters[key[len(model) + 1 :]] = value
  
      return {
          "seed": job.hyperparameters["seed"],
          "hyperparameters": hyperparameters,
      }
  
imports: from __future__ import annotations
  import json
  import math
  import os
  import shutil
  from pathlib import Path
  from typing import Any, cast, Dict, List, Optional, Union
  import numpy
  from tsbench.config import MODEL_REGISTRY
  from tsbench.evaluations.aws import Analysis, TrainingJob
  from tsbench.evaluations.metrics import Metric, Performance
  from tsbench.forecasts import QuantileForecasts
label_desc: Use image node_id 3 for calling a global function with example usage: _extract_configuration(job) and returns: dict