code: def suggest_metrics(
      discriminator=False, feature_extractor=False, loss_weights={}
  ):
      suggested_metrics = {}
      if not discriminator and not feature_extractor:
          suggested_metrics["val_loss"] = "min"
          suggested_metrics["train_loss"] = "min"
          suggested_metrics["val_PSNR"] = "max"
          suggested_metrics["train_PSNR"] = "max"
      if feature_extractor or discriminator:
          suggested_metrics["val_generator_loss"] = "min"
          suggested_metrics["train_generator_loss"] = "min"
          suggested_metrics["val_generator_PSNR"] = "max"
          suggested_metrics["train_generator_PSNR"] = "max"
      if feature_extractor:
          suggested_metrics["val_feature_extractor_loss"] = "min"
          suggested_metrics["train_feature_extractor_loss"] = "min"
      return suggested_metrics
  
file_path: image-super-resolution/ISR/utils/utils.py
imports: import os
  import argparse
  from datetime import datetime
  import numpy
  import yaml
  from ISR.utils.logger import get_logger
label_desc: Use image node_id 13 for calling a global function with example usage: suggest_metrics(discriminator, feature_extractor, loss_weights) and returns: suggested_metrics