code: def model_creator(config):
      model = LSTMSeq2Seq(
          input_feature_num=config["input_feature_num"],
          output_feature_num=config["output_feature_num"],
          future_seq_len=config["future_seq_len"],
          lstm_hidden_dim=config.get("lstm_hidden_dim", 128),
          lstm_layer_num=config.get("lstm_layer_num", 2),
          dropout=config.get("dropout", 0.25),
          teacher_forcing=config.get("teacher_forcing", False),
          seed=config.get("seed", None),
      )
      if config.get("normalization", False):
          model = NormalizeTSModel(model, config["output_feature_num"])
      decomposition_kernel_size = config.get(
          "decomposition_kernel_size", 0
      )
      if decomposition_kernel_size > 1:
          model_copy = LSTMSeq2Seq(
              input_feature_num=config["input_feature_num"],
              output_feature_num=config["output_feature_num"],
              future_seq_len=config["future_seq_len"],
              lstm_hidden_dim=config.get("lstm_hidden_dim", 128),
              lstm_layer_num=config.get("lstm_layer_num", 2),
              dropout=config.get("dropout", 0.25),
              teacher_forcing=config.get("teacher_forcing", False),
              seed=config.get("seed", None),
          )
          if config.get("normalization", False):
              model_copy = NormalizeTSModel(
                  model_copy, config["output_feature_num"]
              )
          model = DecompositionTSModel(
              (model, model_copy), decomposition_kernel_size
          )
      return model
  
file_path: BigDL/python/chronos/src/bigdl/chronos/model/Seq2Seq_pytorch.py
imports: import torch
  import torch.nn
  from .utils import PYTORCH_REGRESSION_LOSS_MAP
  import numpy
  from pytorch_lightning import seed_everything
  from bigdl.chronos.pytorch.model_wrapper.normalization import NormalizeTSModel
  from bigdl.chronos.pytorch.model_wrapper.decomposition import DecompositionTSModel
label_desc: Use image node_id 1 for calling a global function with example usage: model_creator(config) and returns: model