parameters: (self, cid: int, model: ForecastModel)
return_statement: return query_preds
docstring: Output prediction on the test dataset, and segregate the predicted cluster time-series into individual queries
          :param cid: Cluser id
          :param model: Model to use
          :return: Dict of {query_id -> time-series}
docstring_summary: Output prediction on the test dataset, and segregate the predicted cluster time-series into individual queries
          :param cid: Cluser id
          :param model: Model to use
          :return: Dict of {query_id -> time-series}
docstring_tokens: Output prediction on the test dataset and segregate the predicted cluster time - series into individual queries : param cid : Cluser id : param model : Model to use : return : Dict of { query_id - > time - series }
function: def predict(self, cid: int, model: ForecastModel) -> Dict:
          """
          Output prediction on the test dataset, and segregate the predicted cluster time-series into individual queries
          :param cid: Cluser id
          :param model: Model to use
          :return: Dict of {query_id -> time-series}
          """
          test_seqs = self._cluster_seqs(cid, test_mode=True, with_label=False)
          preds = list([model.predict(seq) for seq in test_seqs])
          logging.info(f"Inference preds: {preds}")
          query_preds = self._clusters[cid].segregate(preds)
  
          return query_preds
function_tokens: def predict ( self , cid : int , model : ForecastModel ) -> Dict : test_seqs = self . _cluster_seqs ( cid , test_mode = True , with_label = False ) preds = list ( [ model . predict ( seq ) for seq in test_seqs ] ) logging . info ( f"Inference preds: {preds}" ) query_preds = self . _clusters [ cid ] . segregate ( preds ) return query_preds