class_docstr: The tracker may be used to obtain the performance metrics from a set of
  evaluations.
docstr: Returns the quantile forecasts of all models associated with the
  provided configuration, i.e. forecasts for the same model trained on
  different seeds.
  
  Args:
      config: The configuration to obtain forecasts for.
  
  Returns:
      The list of forecasts for all models.
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code: def get_forecasts(
      self, config: Config[ModelConfig]
  ) -> list[QuantileForecasts]:
      """
      Returns the quantile forecasts of all models associated with the
      provided configuration, i.e. forecasts for the same model trained on
      different seeds.
  
      Args:
          config: The configuration to obtain forecasts for.
  
      Returns:
          The list of forecasts for all models.
      """
      info = self.config_map[config]
      result = []
      for i, job in enumerate(info.jobs):
          result.append(job.get_forecast(info.model_indices[i]))
      return result
  
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 7 for calling the ModelTracker obj's underlying member method code with example usage: obj.get_forecasts(config) and returns: result