code: def save_predictions(self, model, dataset):
      logging.info("Starting the batch predict of the evaluation set")
  
      predictions_ds = model.batch_predict(
          dataset,
          batch_size=self.args.eval_batch_size,
      )
      predictions_ddf = predictions_ds.to_ddf()
  
      if self.args.predict_output_keep_cols:
          cols = set(predictions_ddf.columns)
          pred_cols = sorted(
              list(cols.difference(set(dataset.to_ddf().columns)))
          )
          # Keeping only selected features and all predictions
          predictions_ddf = predictions_ddf[
              self.args.predict_output_keep_cols + pred_cols
          ]
  
      if not self.args.predict_output_path:
          raise Exception(
              "You need to specify the path to save the predictions "
              "using --predict_output_path"
          )
      output_path = self.args.predict_output_path
  
      logging.info(f"Saving predictions to {output_path}")
  
      if self.args.predict_output_format == "parquet":
          predictions_ddf.to_parquet(output_path, write_index=False)
      elif self.args.predict_output_format in ["csv", "tsv"]:
          if self.args.predict_output_format == "csv":
              sep = ","
          elif self.args.predict_output_format == "tsv":
              sep = "\t"
          predictions_ddf.to_csv(
              output_path, single_file=True, index=False, sep=sep
          )
      else:
          raise ValueError(
              "Only supported formats for output prediction files"
              f" are parquet or csv, but got '{self.args.predict_output_format}'"
          )
  
      logging.info(f"Predictions saved to {output_path}")
  
imports: import json
  import logging
  import os
  from datetime import datetime
  import merlin.models.tf
  import numpy
  import tensorflow
  from merlin.io.dataset import Dataset
  from merlin.models.tf.logging.callbacks import ExamplesPerSecondCallback, WandbLogger
  from merlin.models.tf.transforms.negative_sampling import InBatchNegatives
  from merlin.schema.tags import Tags
  from .args_parsing import Task, parse_arguments
  from .mtl import get_mtl_loss_weights, get_mtl_prediction_tasks
  from .ranking_models import get_model
label_desc: Use image node_id 13 for calling the RankingTrainEvalRunner obj's underlying member method code with example usage: obj.save_predictions(model, dataset) without return types