docstr: Prediction.
  :param horizon:
  :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
  :return: result
docstr_tok: ["Prediction",".",":","param","horizon",":",":","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",":","return",":","result"]
code: def predict(
      self,
      horizon=24,
      future_covariates=None,
      future_dti=None,
      num_workers=None,
  ):
      """
      Prediction.
      :param horizon:
      :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
      :return: result
      """
      if num_workers and num_workers != 1:
          raise ValueError(
              "We don't support passing num_workers in predict "
              "with input of xShards of dict"
          )
  
      def orca_predict(data):
          id_arr = data[0]
          tcmf = data[1]
          predict_results = tcmf.predict(
              x=None,
              horizon=horizon,
              future_covariates=future_covariates,
              future_dti=future_dti,
          )
          result = dict()
          result["id"] = id_arr
          result["prediction"] = predict_results
          return result
  
      return self.internal.transform_shard(orca_predict)
  
imports: from abc import ABCMeta, abstractmethod
  from zoo.chronos.model.tcmf import DeepGLO
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.model.abstract import BaseModel
  from zoo.orca.data import SparkXShards, XShards
  import pickle
  import numpy
  import pandas
label_desc: Use image node_id 5 for calling the TCMFXshardsModelWrapper obj's underlying member method code with example usage: obj.predict(horizon, future_covariates, future_dti, num_workers) and returns: self, result