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: Predict horizon time-points ahead the input x in fit_eval
  :param x: We don't support input x currently.
  :param horizon: horizon length to predict
  :param mc:
  :param future_covariates: covariates corresponding to future horizon steps data to predict.
  :param future_dti: dti corresponding to future horizon steps data to predict.
  :param num_workers: the number of workers to use. Note that there has to be an activate
         OrcaRayContext if num_workers > 1.
  :return:
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code: def predict(
      self,
      x=None,
      horizon=24,
      mc=False,
      future_covariates=None,
      future_dti=None,
      num_workers=None,
  ):
      """
      Predict horizon time-points ahead the input x in fit_eval
      :param x: We don't support input x currently.
      :param horizon: horizon length to predict
      :param mc:
      :param future_covariates: covariates corresponding to future horizon steps data to predict.
      :param future_dti: dti corresponding to future horizon steps data to predict.
      :param num_workers: the number of workers to use. Note that there has to be an activate
             OrcaRayContext if num_workers > 1.
      :return:
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if x is not None:
          invalidInputError(False, "We don't support input x directly.")
      if self.model is None:
          invalidInputError(
              False,
              "Needs to call fit_eval or restore first before calling predict",
          )
      self._check_covariates_dti(
          covariates=future_covariates,
          dti=future_dti,
          ts_len=horizon,
          method_name="predict",
      )
      if num_workers is None:
          num_workers = TCMF.get_default_num_workers()
      if num_workers > 1:
          import ray
          from bigdl.orca.ray import OrcaRayContext
  
          try:
              OrcaRayContext.get(initialize=False)
          except:
              try:
                  # detect whether ray has been started.
                  ray.put(None)
              except:
                  invalidInputError(
                      False,
                      f"There must be an activate ray context while running with "
                      f"{num_workers} workers. You can either start and init a "
                      f"RayContext by init_orca_context(..., init_ray_on_spark="
                      f"True) or start Ray with ray.init()",
                  )
  
      out = self.model.predict_horizon(
          future=horizon,
          bsize=90,
          num_workers=num_workers,
          future_covariates=future_covariates,
          future_dti=future_dti,
      )
      return out[:, -horizon::]
  
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 6 for calling the TCMF obj's underlying member method code with example usage: obj.predict(x, horizon, mc, future_covariates, future_dti, num_workers) and returns: out