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 fit_incr(self, data, validation_data=None, **kwargs):
      from bigdl.nano.utils.common import invalidInputError
  
      invalidInputError(
          self.built,
          "You must call setup or restore before calling fit_eval",
      )
      if not isinstance(data, pd.DataFrame):
          invalidInputError(
              False,
              f"We only support data of pd.DataFrame. "
              f"Got data of {data.__class__.__name__}",
          )
      if validation_data is not None and not isinstance(
          validation_data, pd.DataFrame
      ):
          invalidInputError(
              False,
              f"We only support validation_data of pd.DataFrame. "
              f"Got validation_data of {data.__class__.__name__}",
          )
      data_np = self._process_data(data, mode="val")
      is_val_valid = (
          isinstance(validation_data, pd.DataFrame)
          and not validation_data.empty
      )
      if is_val_valid:
          val_data_np = self._process_data(data, mode="val")
      else:
          # this is a work around since pytorch base model must include validation data for
          # fit_eval. We may need to optimize automl base model interface.
          val_data_np = data_np
  
      return self.model.fit_eval(
          data=data_np, validation_data=val_data_np, **kwargs
      )
  
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 6 for calling the TimeSequenceModel obj's underlying member method code with example usage: obj.fit_incr(data, validation_data) and returns: self