code: def evaluation(
      self,
      dataset,
      dataset_name,
      metrics_log,
      batch_size,
      progress_tracker,
  ):
      predictor = LlmFineTunePredictor(
          self.model,
          batch_size=batch_size,
          distributed=self.distributed,
          report_tqdm_to_ray=self.report_tqdm_to_ray,
      )
      metrics, _, input_target_output_dict = predictor.batch_evaluation(
          dataset, collect_predictions=False, dataset_name=dataset_name
      )
      # Setting collect_predictions=True currently causes an error when doing batch evaluation because the outputs
      # can be of variable sizes but we try to concatenate them into a single tensor.
  
      tokenizer = self.dist_model.tokenizer
  
      # There should only be one key in the dict for LLMs
      input_key = list(input_target_output_dict["inputs"].keys())[0]
      num_examples = min(
          len(input_target_output_dict["inputs"][input_key]),
          MAX_EVALUATION_EXAMPLES,
      )
  
      llm_eval_examples = {"inputs": [], "targets": [], "outputs": []}
      for key in input_target_output_dict["inputs"]:
          for inp in input_target_output_dict["inputs"][key][
              :num_examples
          ]:
              llm_eval_examples["inputs"].append(
                  tokenizer.decode(inp, skip_special_tokens=True)
              )
  
      for key in input_target_output_dict["targets"]:
          for tar in input_target_output_dict["targets"][key][
              :num_examples
          ]:
              llm_eval_examples["targets"].append(
                  tokenizer.decode(tar, skip_special_tokens=True)
              )
  
      for key in input_target_output_dict["outputs"]:
          for out in input_target_output_dict["outputs"][key][
              :num_examples
          ]:
              llm_eval_examples["outputs"].append(
                  tokenizer.decode(out, skip_special_tokens=True)
              )
  
      num_examples_shown = min(
          len(llm_eval_examples["inputs"]),
          MAX_EVALUATION_EXAMPLES_SHOWN,
      )
      for i in range(num_examples_shown):
          logger.info(
              f"Input: {llm_eval_examples['inputs'][i].strip()}"
          )
          logger.info(
              f"Output: {llm_eval_examples['outputs'][i].strip()}"
          )
          logger.info("--------------------")
  
      progress_tracker.llm_eval_examples = llm_eval_examples
      return append_metrics(
          self.model,
          dataset_name,
          metrics,
          metrics_log,
          progress_tracker,
      )
  
imports: import logging
  import os
  import time
  from typing import Callable, Dict, List, Optional, Union
  from torch.utils.tensorboard import SummaryWriter
  from ludwig.constants import MINIMUM_BATCH_SIZE, TEST, TRAINING, VALIDATION
  from ludwig.data.dataset.base import Dataset
  from ludwig.distributed.base import DistributedStrategy, LocalStrategy
  from ludwig.features.feature_utils import LudwigFeatureDict
  from ludwig.models.llm import LLM
  from ludwig.models.predictor import LlmFineTunePredictor, LlmPredictor
  from ludwig.modules.metric_modules import get_initial_validation_value
  from ludwig.schema.trainer import BaseTrainerConfig, FineTuneTrainerConfig, NoneTrainerConfig
  from ludwig.trainers.base import BaseTrainer
  from ludwig.trainers.registry import register_llm_ray_trainer, register_llm_trainer
  from ludwig.trainers.trainer import Trainer
  from ludwig.types import ModelConfigDict
  from ludwig.utils import time_utils
  from ludwig.utils.defaults import default_random_seed
  from ludwig.utils.metric_utils import TrainerMetric
  from ludwig.utils.metrics_printed_table import print_metrics_table
  from ludwig.utils.misc_utils import set_random_seed
  from ludwig.utils.torch_utils import get_torch_device
  from ludwig.utils.trainer_utils import append_metrics, get_new_progress_tracker, ProgressTracker
label_desc: Use image node_id 3 for calling the FineTuneTrainer obj's underlying member method code with example usage: obj.evaluation(dataset, dataset_name, metrics_log, batch_size, progress_tracker) and returns: append_metrics