code: def log_metrics(self):
      log_metrics = {
          "batch_size": self.batch_size,
          "epoch": self.epoch,
          "steps": self.steps,
          "tune_checkpoint_num": self.tune_checkpoint_num,
          "checkpoint_number": self.checkpoint_number,
          "last_improvement_steps": self.last_improvement_steps,
          "best_eval_metric_steps": self.best_eval_metric_steps,
          "best_eval_metric_epoch": self.best_eval_metric_epoch,
          "best_eval_metric_checkpoint_number": self.best_eval_metric_checkpoint_number,
          "learning_rate": self.learning_rate,
          "best_valid_metric": self.best_eval_metric_value,
          "num_reductions_lr": self.num_reductions_learning_rate,
          "num_increases_bs": self.num_increases_batch_size,
          "total_tokens_used": self.total_tokens_used,
      }
  
      # This is a non-numerical metric that is only for LLM fine-tuning
      # This should be an dictionary whose keys are "inputs", "targets", and "outputs" and whose values are dicts.
      # The keys of each subdict are the names of the input/target/output features and the values are lists of
      # example tensors.
      if self.llm_eval_examples:
          log_metrics["llm_eval_examples"] = self.llm_eval_examples
  
      for metrics_dict_name in [
          "train_metrics",
          "validation_metrics",
          "test_metrics",
      ]:
          metrics_dict = getattr(self, metrics_dict_name)
          for feature_name in metrics_dict:
              for metric_name, metrics_tuples in metrics_dict[
                  feature_name
              ].items():
                  if metrics_tuples:
                      # For logging, get the latest metrics. The second "-1" indexes into the TrainerMetric
                      # namedtuple. The last element of the TrainerMetric namedtuple is the actual metric value.
                      #
                      # TODO: when loading an existing model, this loses metric values for all but the last epoch.
                      log_metrics[
                          f"{metrics_dict_name}.{feature_name}.{metric_name}"
                      ] = metrics_tuples[-1][-1]
  
      # Add best metrics.
      for feature_name, metrics in self.best_eval_train_metrics.items():
          for metric_name, metric_value in metrics.items():
              log_metrics[
                  f"best.train_metrics.{feature_name}.{metric_name}"
              ] = metric_value
      for (
          feature_name,
          metrics,
      ) in self.best_eval_validation_metrics.items():
          for metric_name, metric_value in metrics.items():
              log_metrics[
                  f"best.validation_metrics.{feature_name}.{metric_name}"
              ] = metric_value
      for feature_name, metrics in self.best_eval_test_metrics.items():
          for metric_name, metric_value in metrics.items():
              log_metrics[
                  f"best.test_metrics.{feature_name}.{metric_name}"
              ] = metric_value
  
      return log_metrics
  
imports: import logging
  from collections import defaultdict
  from typing import Dict, List, Tuple, TYPE_CHECKING
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import AUTO, COMBINED, LOSS
  from ludwig.models.base import BaseModel
  from ludwig.modules.metric_modules import get_best_function
  from ludwig.utils.data_utils import save_json
  from ludwig.utils.metric_utils import TrainerMetric
label_desc: Use image node_id 4 for calling the ProgressTracker obj's underlying member method code with example usage: obj.log_metrics() and returns: log_metrics