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.
class_docstr_tok: ["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 setup(self, config):
      # setup self.config, self.model, self.built
      self.config = config.copy()
      # add configs for model
      self.config["future_seq_len"] = self.ft.future_seq_len
      self.config["check_optional_config"] = False
      # for base keras model
      self.config["input_dim"] = self.ft.get_feature_dim()
      self.config["output_dim"] = self.ft.get_target_dim()
      # for base pytorch model
      self.config["input_feature_num"] = self.ft.get_feature_dim()
      self.config["output_feature_num"] = self.ft.get_target_dim()
  
      if not self.model:
          self.model = TimeSequenceModel._sel_model(self.config)
      # self.model.build(self.config)
      self.built = True
  
imports: from copy import deepcopy
  from zoo.orca.automl.model.abstract import BaseModel, ModelBuilder
  from zoo.chronos.model.VanillaLSTM import VanillaLSTM
  from zoo.chronos.model.Seq2Seq import LSTMSeq2Seq
  from zoo.chronos.model.MTNet_keras import MTNetKeras
  from zoo.chronos.autots.deprecated.feature.utils import save_config
  from zoo.chronos.autots.deprecated.feature.time_sequence import TimeSequenceFeatureTransformer
  from zoo.chronos.autots.deprecated.preprocessing.impute import LastFillImpute, FillZeroImpute
  from zoo.chronos.utils import deprecated
  from zoo.orca.automl.metrics import Evaluator
  import pandas
  import os
  import tempfile
  import shutil
  import zipfile
  import json
label_desc: Use image node_id 3 for calling the TimeSequenceModel obj's underlying member method code with example usage: obj.setup(config) without return types