code: def trained_learners():
      learners = {}
      model_paths = []
      for hp in TEST_HYPERPARAMETER_SETTINGS:
          temp_model_path = tempfile.mkdtemp()
          learner = TimeSeriesLearner(
              path_context=temp_model_path,
              eval_metric="MASE",
              prediction_length=3,
          )
          learner.fit(
              train_data=DUMMY_TS_DATAFRAME,
              hyperparameters=hp,
          )
          learners[repr(hp)] = learner
          model_paths.append(temp_model_path)
  
      yield learners
  
      for td in model_paths:
          shutil.rmtree(td)
  
imports: import shutil
  import sys
  import tempfile
  from collections import defaultdict
  from unittest import mock
  import numpy
  import pytest
  from autogluon.common import space
  from autogluon.timeseries.dataset import TimeSeriesDataFrame
  from autogluon.timeseries.learner import TimeSeriesLearner
  from autogluon.timeseries.models import DeepARModel, ETSModel
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_single_time_series
  from .common import DUMMY_TS_DATAFRAME, get_data_frame_with_variable_lengths, get_static_features
label_desc: Use image node_id 1 for calling a global function with example usage: trained_learners() without return types