code: def __init__(
      self,
      ensemble_size: int,
      metric: TimeSeriesScorer,
      problem_type: str = ag.constants.QUANTILE,
      sorted_initialization: bool = False,
      bagging: bool = False,
      tie_breaker: str = "random",
      random_state: np.random.RandomState = None,
      prediction_length: int = 1,
      target: str = "target",
      eval_metric_seasonal_period: Optional[int] = None,
      **kwargs,
  ):
      super().__init__(
          ensemble_size=ensemble_size,
          metric=metric,
          problem_type=problem_type,
          sorted_initialization=sorted_initialization,
          bagging=bagging,
          tie_breaker=tie_breaker,
          random_state=random_state,
          **kwargs,
      )
      self.prediction_length = prediction_length
      self.target = target
      self.eval_metric_seasonal_period = eval_metric_seasonal_period
  
imports: import copy
  import logging
  import pprint
  from typing import Dict, List, Optional
  import numpy
  import autogluon.core
  from autogluon.common.utils.log_utils import set_logger_verbosity
  from autogluon.core.models.greedy_ensemble.ensemble_selection import EnsembleSelection
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.metrics import TimeSeriesScorer
  from autogluon.timeseries.models.ensemble import AbstractTimeSeriesEnsembleModel
  from autogluon.timeseries.utils.datetime import get_seasonality
label_desc: Use image node_id 1 to create a new TimeSeriesEnsembleSelection object from inherited base classes: EnsembleSelection with example: obj = TimeSeriesEnsembleSelection(ensemble_size, metric, problem_type, sorted_initialization, bagging, tie_breaker, random_state, prediction_length, target, eval_metric_seasonal_period)