class_docstr: MF regularized TCN + TCN. This version is not for automated searching yet.
class_docstr_tok: ["MF","regularized","TCN","+","TCN",".","This","version","is","not","for","automated","searching","yet","."]
docstr: build the models and initialize.
  :param config: hyper parameters for building the model
  :return:
docstr_tok: ["build","the","models","and","initialize",".",":","param","config",":","hyper","parameters","for","building","the","model",":","return",":"]
code: def build(self, config):
      """
      build the models and initialize.
      :param config: hyper parameters for building the model
      :return:
      """
      self.model = DeepGLO(
          vbsize=config.get("vbsize", 128),
          hbsize=config.get("hbsize", 256),
          num_channels_X=config.get(
              "num_channels_X", [32, 32, 32, 32, 32, 1]
          ),
          num_channels_Y=config.get(
              "num_channels_Y", [16, 16, 16, 16, 16, 1]
          ),
          kernel_size=config.get("kernel_size", 7),
          dropout=config.get("dropout", 0.1),
          rank=config.get("rank", 64),
          kernel_size_Y=config.get("kernel_size_Y", 7),
          lr=config.get("learning_rate", 0.0005),
          normalize=config.get("normalize", False),
          use_time=config.get("use_time", True),
          svd=config.get("svd", True),
          forward_cov=False,
      )
      self.model_init = True
  
imports: from abc import ABCMeta, abstractmethod
  from bigdl.chronos.model.tcmf import DeepGLO
  from bigdl.orca.automl.metrics import Evaluator
  from bigdl.orca.automl.model.abstract import BaseModel
  from bigdl.orca.data import SparkXShards, XShards
  import pickle
  import numpy
  import pandas
label_desc: Use image node_id 2 for calling the TCMF obj's underlying member method code with example usage: obj.build(config) without return types