class_docstr: The tracker may be used to obtain the performance metrics from a set of
  evaluations.
docstr: Returns the unique model configurations that are available in the
  experiments managed by this tracker.
  
  Returns:
      The list of available model configurations.
docstr_tok: ["Returns","the","unique","model","configurations","that","are","available","in","the","experiments","managed","by","this","tracker",".","Returns",":","The","list","of","available","model","configurations","."]
code: def unique_model_configs(self) -> list[ModelConfig]:
      """
      Returns the unique model configurations that are available in the
      experiments managed by this tracker.
  
      Returns:
          The list of available model configurations.
      """
      return list({c.model for c in self.config_map.keys()})
  
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 5 for calling the ModelTracker obj's underlying member method code with example usage: obj.unique_model_configs() and returns: list