class_docstr: A job provides data available for a single training job.
  
  It is first obtained from a training job run on AWS Sagemaker. By storing
  it locally, it can be loaded from the file system at a later point and no
  Sagemaker connection is required. The initializer of this class should not
  be used directly.
class_docstr_tok: ["A","job","provides","data","available","for","a","single","training","job",".","It","is","first","obtained","from","a","training","job","run","on","AWS","Sagemaker",".","By","storing","it","locally",",","it","can","be","loaded","from","the","file","system","at","a","later","point","and","no","Sagemaker","connection","is","required",".","The","initializer","of","this","class","should","not","be","used","directly","."]
docstr: Returns the list of performances for all models associated with this
  job.
  
  The variances of all metrics will be set to 0.
docstr_tok: ["Returns","the","list","of","performances","for","all","models","associated","with","this","job",".","The","variances","of","all","metrics","will","be","set","to","0","."]
code: def performances(self) -> list[Performance]:
      """
      Returns the list of performances for all models associated with this
      job.
  
      The variances of all metrics will be set to 0.
      """
      return [
          Performance(
              training_time=Metric(p["training"]["duration"], 0),
              latency=Metric(self.static_metrics["latency"], 0),
              num_model_parameters=Metric(
                  self.static_metrics["num_model_parameters"], 0
              ),
              num_gradient_updates=Metric(
                  p["training"]["num_gradient_updates"], 0
              ),
              **{
                  k: Metric(p["testing"][k], 0)
                  for k in ["mase", "smape", "nrmse", "nd", "ncrps"]
              },
          )
          for p in self.metrics
      ]
  
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 7 for calling the Job obj's underlying member method code with example usage: obj.performances() and returns: unknown