code: def do_evaluate(pred_config, output_file):
      num_tower = max(cfg.TRAIN.NUM_GPUS, 1)
      graph_funcs = MultiTowerOfflinePredictor(
          pred_config, list(range(num_tower))
      ).get_predictors()
  
      for dataset in cfg.DATA.VAL:
          logger.info("Evaluating {} ...".format(dataset))
          dataflows = [
              get_eval_dataflow(dataset, shard=k, num_shards=num_tower)
              for k in range(num_tower)
          ]
          all_results = multithread_predict_dataflow(
              dataflows, graph_funcs
          )
          output = output_file + "-" + dataset
          DatasetRegistry.get(dataset).eval_inference_results(
              all_results, output
          )
  
imports: import argparse
  import itertools
  import numpy
  import os
  import shutil
  import tensorflow
  import cv2
  import tqdm
  import tensorpack.utils.viz
  from tensorpack.predict import MultiTowerOfflinePredictor, OfflinePredictor, PredictConfig
  from tensorpack.tfutils import SmartInit, get_tf_version_tuple
  from tensorpack.tfutils.export import ModelExporter
  from tensorpack.utils import fs, logger
  from dataset import DatasetRegistry, register_coco, register_balloon
  from config import config
  from config import finalize_configs
  from data import get_eval_dataflow, get_train_dataflow
  from eval import DetectionResult, multithread_predict_dataflow, predict_image
  from modeling.generalized_rcnn import ResNetC4Model, ResNetFPNModel
  from viz import draw_annotation, draw_final_outputs, draw_predictions, draw_proposal_recall, draw_final_outputs_blackwhite
label_desc: Use image node_id 2 for calling a global function with example usage: do_evaluate(pred_config, output_file) without return types