code: def get_metrics(self):
      metrics = dict()
      if Task.BINARY_CLASSIFICATION.value in self.targets:
          metrics.update(
              {
                  f"{target}/binary_output": [
                      tf.keras.metrics.AUC(
                          name="auc",
                          curve="ROC",
                          num_thresholds=int(1e5),
                      ),
                      tf.keras.metrics.AUC(
                          name="prauc",
                          curve="PR",
                          num_thresholds=int(1e5),
                      ),
                      mm.LogLossMetric(name="logloss"),
                  ]
                  for target in self.targets[
                      Task.BINARY_CLASSIFICATION.value
                  ]
              }
          )
  
      if Task.REGRESSION.value in self.targets:
          metrics.update(
              {
                  f"{target}/regression_output": "rmse"
                  for target in self.targets[Task.REGRESSION.value]
              }
          )
  
      if len(metrics) == 1:
          return list(metrics.values())[0]
      else:
          return metrics
  
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 7 for calling the RankingTrainEvalRunner obj's underlying member method code with example usage: obj.get_metrics() and returns: list, metrics