class_docstr: Time Sequence Model integrates feature transformation model selection for time series
  forecasting.
  It has similar functionality with the TimeSequencePipeline.
  Note that to be compatible with load_ts_pipeline in TimeSequencePipeline,
  TimeSequenceModel should be able to restore with TimeSequenceModel().restore(checkpoint).
  TimeSequenceModel could be optimized if we deprecate load_ts_pipeline
   in future version.
code: def _sel_model(config, verbose=0):
      model_name = config.get("model", "LSTM")
      model = MODEL_MAP[model_name](
          check_optional_config=config.get(
              "check_optional_config", False
          )
      )
      if verbose != 0:
          print(model_name, "is selected.")
      return model
  
imports: from copy import deepcopy
  from bigdl.orca.automl.model.abstract import BaseModel, ModelBuilder
  from bigdl.chronos.model.tf1.VanillaLSTM_keras import VanillaLSTM
  from bigdl.chronos.model.tf1.Seq2Seq_keras import LSTMSeq2Seq
  from bigdl.chronos.model.tf1.MTNet_keras import MTNetKeras
  from bigdl.chronos.autots.deprecated.feature.utils import save_config
  from bigdl.chronos.autots.deprecated.feature.time_sequence import TimeSequenceFeatureTransformer
  from bigdl.chronos.autots.deprecated.preprocessing.impute import LastFillImpute, FillZeroImpute
  from bigdl.chronos.utils import deprecated
  from bigdl.orca.automl.metrics import Evaluator
  import pandas
  import os
  import tempfile
  import shutil
  import zipfile
  import json
label_desc: Use image node_id 7 for calling the TimeSequenceModel obj's underlying member method code with example usage: obj._sel_model(config, verbose) and returns: model