code: def test_train_shell(
      train_env: TrainEnv, caplog, forecaster_type
  ) -> None:
      caplog.set_level(logging.INFO)
  
      run_train_and_test(env=train_env, forecaster_type=forecaster_type)
  
      if forecaster_type == MeanPredictor:
          for _, _, line in caplog.record_tuples:
              if "#test_score (local, QuantileLoss" in line:
                  assert line.endswith("0.0")
              if "local, wQuantileLoss" in line:
                  assert line.endswith("0.0")
              if "local, Coverage" in line:
                  assert line.endswith("1.0")
              if "MASE" in line or "MSIS" in line:
                  assert line.endswith("nan")
              if "abs_target_sum" in line:
                  assert line.endswith("270.0")
  
imports: import json
  import logging
  import sys
  from typing import ContextManager
  import numpy
  import pytest
  from gluonts.core.component import equals
  from gluonts.dataset.jsonl import encode_json
  from gluonts.model.trivial.mean import MeanPredictor
  from gluonts.shell.env import ServeEnv, TrainEnv
  from gluonts.shell.train import run_train_and_test
label_desc: Use image node_id 6 for calling a global function with example usage: test_train_shell(train_env, caplog, forecaster_type) without return types