code: def evaluate_from_args(args):
      # Disable some of the more verbose logging statements
      logging.getLogger("allennlp.common.params").disabled = True
      logging.getLogger("allennlp.nn.initializers").disabled = True
      logging.getLogger(
          "allennlp.modules.token_embedders.embedding"
      ).setLevel(logging.INFO)
  
      # Load from archive
      archive = load_archive(
          args.archive_file,
          args.cuda_device,
          args.overrides,
          args.weights_file,
      )
      config = archive.config
      prepare_environment(config)
      model = archive.model
      model.eval()
  
      # Load the evaluation data
  
      # Try to use the validation dataset reader if there is one - otherwise fall back
      # to the default dataset_reader used for both training and validation.
      validation_dataset_reader_params = config.pop(
          "validation_dataset_reader", None
      )
      if validation_dataset_reader_params is not None:
          dataset_reader = DatasetReader.from_params(
              validation_dataset_reader_params
          )
      else:
          dataset_reader = DatasetReader.from_params(
              config.pop("dataset_reader")
          )
      evaluation_data_path = args.input_file
      logger.info(
          "Reading evaluation data from %s", evaluation_data_path
      )
      instances = dataset_reader.read(evaluation_data_path)
  
      iterator_params = config.pop("validation_iterator", None)
      if iterator_params is None:
          iterator_params = config.pop("iterator")
      iterator = DataIterator.from_params(iterator_params)
      iterator.index_with(model.vocab)
  
      metrics = evaluate(model, instances, iterator, args.cuda_device)
  
      logger.info("Finished evaluating.")
      logger.info("Metrics:")
      for key, metric in list(metrics.items()):
          logger.info("%s: %s", key, metric)
  
      output_file = args.output_file
      if output_file:
          with open(output_file, "w") as file:
              json.dump(metrics, file, indent=4)
      return metrics
  
imports: from __future__ import with_statement
  from __future__ import absolute_import
  import argparse
  import logging
  import json
  import torch
  from allennlp.commands.subcommand import Subcommand
  from allennlp.common.checks import check_for_gpu
  from allennlp.common.util import prepare_environment
  from allennlp.common.tqdm import Tqdm
  from allennlp.data import Instance
  from allennlp.data.dataset_readers.dataset_reader import DatasetReader
  from allennlp.data.iterators import DataIterator
  from allennlp.models.archival import load_archive
  from allennlp.models.model import Model
  from io import open
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate_from_args(args) and returns: metrics