code: def evaluate(
      model: Model,
      data_loader: DataLoader,
      cuda_device: Union[int, torch.device] = -1,
      batch_weight_key: str = None,
      output_file: str = None,
      predictions_output_file: str = None,
  ) -> Dict[str, Any]:
      """
      # Parameters
  
      model : `Model`
          The model to evaluate
      data_loader : `DataLoader`
          The `DataLoader` that will iterate over the evaluation data (data loaders already contain
          their data).
      cuda_device : `Union[int, torch.device]`, optional (default=`-1`)
          The cuda device to use for this evaluation.  The model is assumed to already be using this
          device; this parameter is only used for moving the input data to the correct device.
      batch_weight_key : `str`, optional (default=`None`)
          If given, this is a key in the output dictionary for each batch that specifies how to weight
          the loss for that batch.  If this is not given, we use a weight of 1 for every batch.
      metrics_output_file : `str`, optional (default=`None`)
          Optional path to write the final metrics to.
      predictions_output_file : `str`, optional (default=`None`)
          Optional path to write the predictions to.
  
      # Returns
  
      `Dict[str, Any]`
          The final metrics.
      """
      check_for_gpu(cuda_device)
      data_loader.set_target_device(int_to_device(cuda_device))
      predictions_file = (
          None
          if predictions_output_file is None
          else open(predictions_output_file, "w")
      )
  
      with torch.no_grad():
          model.eval()
  
          iterator = iter(data_loader)
          logger.info("Iterating over dataset")
          generator_tqdm = Tqdm.tqdm(iterator)
  
          # Number of batches in instances.
          batch_count = 0
          # Number of batches where the model produces a loss.
          loss_count = 0
          # Cumulative weighted loss
          total_loss = 0.0
          # Cumulative weight across all batches.
          total_weight = 0.0
  
          for batch in generator_tqdm:
              batch_count += 1
              batch = nn_util.move_to_device(batch, cuda_device)
              output_dict = model(**batch)
              loss = output_dict.get("loss")
  
              metrics = model.get_metrics()
  
              if loss is not None:
                  loss_count += 1
                  if batch_weight_key:
                      weight = output_dict[batch_weight_key].item()
                  else:
                      weight = 1.0
  
                  total_weight += weight
                  total_loss += loss.item() * weight
                  # Report the average loss so far.
                  metrics["loss"] = total_loss / total_weight
  
              if not HasBeenWarned.tqdm_ignores_underscores and any(
                  metric_name.startswith("_") for metric_name in metrics
              ):
                  logger.warning(
                      'Metrics with names beginning with "_" will '
                      "not be logged to the tqdm progress bar."
                  )
                  HasBeenWarned.tqdm_ignores_underscores = True
              description = (
                  ", ".join(
                      [
                          "%s: %.2f" % (name, value)
                          for name, value in metrics.items()
                          if not name.startswith("_")
                      ]
                  )
                  + " ||"
              )
              generator_tqdm.set_description(description, refresh=False)
  
              if predictions_file is not None:
                  predictions = json.dumps(
                      sanitize(
                          model.make_output_human_readable(output_dict)
                      )
                  )
                  predictions_file.write(predictions + "\n")
  
          if predictions_file is not None:
              predictions_file.close()
  
          final_metrics = model.get_metrics(reset=True)
          if loss_count > 0:
              # Sanity check
              if loss_count != batch_count:
                  raise RuntimeError(
                      "The model you are trying to evaluate only sometimes produced a loss!"
                  )
              final_metrics["loss"] = total_loss / total_weight
  
          if output_file is not None:
              dump_metrics(output_file, final_metrics, log=True)
  
          return final_metrics
  
imports: import datetime
  import logging
  import os
  import shutil
  import json
  from os import PathLike
  from typing import Any, Dict, Iterable, Optional, Union, Tuple, Set, List
  from collections import Counter
  import torch
  from torch.nn.utils import clip_grad_norm_
  from allennlp.common.checks import check_for_gpu, ConfigurationError
  from allennlp.common.params import Params
  from allennlp.common.tqdm import Tqdm
  from allennlp.common.util import dump_metrics, sanitize, int_to_device
  from allennlp.data import Instance, Vocabulary, Batch, DataLoader
  from allennlp.data.dataset_readers import DatasetReader
  from allennlp.models.archival import CONFIG_NAME
  from allennlp.models.model import Model
  from allennlp.nn import util
label_desc: Use image node_id 11 for calling a global function with example usage: evaluate(model, data_loader, cuda_device, batch_weight_key, output_file, predictions_output_file) and returns: final_metrics