code: def add_subparser(
      self, parser: argparse._SubParsersAction
  ) -> argparse.ArgumentParser:
      description = """Evaluate the specified model + dataset(s)"""
      subparser = parser.add_parser(
          self.name,
          description=description,
          help="Evaluate the specified model + dataset(s).",
      )
  
      subparser.add_argument(
          "archive_file",
          type=str,
          help="path to an archived trained model",
      )
  
      subparser.add_argument(
          "input_file",
          type=str,
          help="path to the file containing the evaluation data (for multiple "
          "files, put between filenames e.g., input1.txt,input2.txt)",
      )
  
      subparser.add_argument(
          "--output-file",
          type=str,
          help="optional path to write the metrics to as JSON (for multiple "
          "files, put  between filenames e.g., output1.txt,output2.txt)",
      )
  
      subparser.add_argument(
          "--predictions-output-file",
          type=str,
          help="optional path to write the predictions to as JSON lines "
          "(for mutiple files, put  between filenames e.g., "
          "output1.jsonl,output2.jsonl)",
      )
  
      subparser.add_argument(
          "--weights-file",
          type=str,
          help="a path that overrides which weights file to use",
      )
  
      cuda_device = subparser.add_mutually_exclusive_group(
          required=False
      )
      cuda_device.add_argument(
          "--cuda-device",
          type=int,
          default=-1,
          help="id of GPU to use (if any)",
      )
  
      subparser.add_argument(
          "-o",
          "--overrides",
          type=str,
          default="",
          help=(
              "a json(net) structure used to override the experiment configuration, e.g., "
              "'{\"iterator.batch_size\": 16}'.  Nested parameters can be specified either"
              " with nested dictionaries or with dot syntax."
          ),
      )
  
      subparser.add_argument(
          "--batch-size",
          type=int,
          help="If non-empty, the batch size to use during evaluation.",
      )
  
      subparser.add_argument(
          "--batch-weight-key",
          type=str,
          default="",
          help="If non-empty, name of metric used to weight the loss on a per-batch basis.",
      )
  
      subparser.add_argument(
          "--extend-vocab",
          action="store_true",
          default=False,
          help="if specified, we will use the instances in your new dataset to "
          "extend your vocabulary. If pretrained-file was used to initialize "
          "embedding layers, you may also need to pass --embedding-sources-mapping.",
      )
  
      subparser.add_argument(
          "--embedding-sources-mapping",
          type=str,
          default="",
          help="a JSON dict defining mapping from embedding module path to embedding "
          "pretrained-file used during training. If not passed, and embedding needs to be "
          "extended, we will try to use the original file paths used during training. If "
          "they are not available we will use random vectors for embedding extension.",
      )
      subparser.add_argument(
          "--file-friendly-logging",
          action="store_true",
          default=False,
          help="outputs tqdm status on separate lines and slows tqdm refresh rate",
      )
  
      subparser.add_argument(
          "--auto-names",
          default="NONE",
          help="Automatically create output names for each evaluation file. "
          "`NONE` will not automatically generate a file name for the "
          "neither the metrics nor the predictions. In this case you will"
          " need to pas in both `metrics_output_file` and `predictions_output_file`. "
          "`METRICS` will only automatically create a file name for the"
          " metrics file. `PREDS` will only automatically create a file"
          " name for the predictions outputs. `ALL` will create a "
          "filename for both the metrics and the predictions.",
          choices=["NONE", "METRICS", "PREDS", "ALL"],
      )
  
      subparser.set_defaults(func=evaluate_from_args)
  
      return subparser
  
file_path: allennlp/allennlp/commands/evaluate.py
imports: import argparse
  import json
  import logging
  from json import JSONDecodeError
  from pathlib import Path
  from os import PathLike
  from typing import Union, Dict, Any, Optional
  from copy import deepcopy
  from allennlp.commands.subcommand import Subcommand
  from allennlp.common import logging
  from allennlp.common.util import prepare_environment
  from allennlp.data import DataLoader
  from allennlp.models.archival import load_archive
  from allennlp.evaluation import Evaluator
label_desc: Use image node_id 1 for calling the Evaluate obj's underlying member method code with example usage: obj.add_subparser(parser) and returns: subparser