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","."]
docstr: Prediction on x.
  :param df: input
  :return: predicted y
docstr_tok: ["Prediction","on","x.",":","param","df",":","input",":","return",":","predicted","y"]
code: def predict(self, df):
      """
      Prediction on x.
      :param df: input
      :return: predicted y
      """
      data_np = self._process_data(df, mode="test")
      y_pred = self.model.predict(data_np)
      output = self.ft.post_processing(df, y_pred, is_train=False)
      return output
  
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 9 for calling the TimeSequenceModel obj's underlying member method code with example usage: obj.predict(df) and returns: output