class_docstr: The tracker may be used to obtain the performance metrics from a set of
  evaluations.
docstr: Returns all training jobs associated with the provided configuration.
  
  Args:
      config: The model and dataset configuration.
  
  Returns:
      The list of all training jobs.
docstr_tok: ["Returns","all","training","jobs","associated","with","the","provided","configuration",".","Args",":","config",":","The","model","and","dataset","configuration",".","Returns",":","The","list","of","all","training","jobs","."]
code: def get_training_jobs(self, config: Config[ModelConfig]) -> list[Job]:
      """
      Returns all training jobs associated with the provided configuration.
  
      Args:
          config: The model and dataset configuration.
  
      Returns:
          The list of all training jobs.
      """
      return self.config_map[config].jobs
  
imports: from __future__ import annotations
  import pickle
  from pathlib import Path
  from typing import Any, List, Optional
  from tsbench.config import Config, ModelConfig
  from tsbench.constants import DEFAULT_DATA_PATH
  from tsbench.evaluations import aws
  from tsbench.evaluations.metrics import Performance
  from tsbench.forecasts.quantile import QuantileForecasts
  from ._base import Tracker
  from ._evaluations import Evaluations
  from ._info import extract_job_infos, ValidationMetric, ValidationScores
  from .job import Job, load_jobs_from_analysis, load_jobs_from_directory
label_desc: Use image node_id 6 for calling the ModelTracker obj's underlying member method code with example usage: obj.get_training_jobs(config) and returns: self