code: def get_evaluator():
      """Evaluator."""
      evaluator = evaluator_util.Evaluator(
          {
              # 'shape_accuracy_topk1': evaluator_util.ShapeAccuracyMetric(k=1),
              # 'shape_accuracy_topk10': evaluator_util.ShapeAccuracyMetric(k=10),
              # 'shape_accuracy_topk100': evaluator_util.ShapeAccuracyMetric(k=100),
              "2D_mAP_25": evaluator_util.BoxIoUMetric(
                  t=0.25, threed=False
              ),
              "2D_mAP_50": evaluator_util.BoxIoUMetric(
                  t=0.50, threed=False
              ),
              # '2D_mAP_60': evaluator_util.BoxIoUMetric(t=0.60, threed=False),
              # '2D_mAP_75': evaluator_util.BoxIoUMetric(t=0.75, threed=False),
              # '2D_mAP_80': evaluator_util.BoxIoUMetric(t=0.80, threed=False),
              # '2D_mAP_90': evaluator_util.BoxIoUMetric(t=0.90, threed=False),
              "3D_mAP_25": evaluator_util.BoxIoUMetric(
                  t=0.25, threed=True
              ),
              "3D_mAP_50": evaluator_util.BoxIoUMetric(
                  t=0.50, threed=True
              ),
              "3D_IoU": evaluator_util.IoUMetric(
                  max_num_classes=FLAGS.num_classes,
                  tol=FLAGS.tol,
                  resolution=64,
              ),
              # 'Collision': evaluator_util.CollisionMetric(tol=-FLAGS.tol),
              # '3D_mAP_60': evaluator_util.BoxIoUMetric(t=0.60, threed=True),
              # '3D_mAP_75': evaluator_util.BoxIoUMetric(t=0.75, threed=True),
              # '3D_mAP_80': evaluator_util.BoxIoUMetric(t=0.80, threed=True),
              # '3D_mAP_90': evaluator_util.BoxIoUMetric(t=0.90, threed=True)
          },
          split=FLAGS.split,
          shapenet_dir=FLAGS.shapenet_dir,
      )
      slave = FLAGS.part_id > -1
      if slave:
          iou_path = os.path.join(
              FLAGS.logdir,
              FLAGS.metrics_dir,
              "iou",
              "iou_" + str(FLAGS.part_id).zfill(4) + ".pkl",
          )
          if not tf.io.gfile.exists(os.path.dirname(iou_path)):
              tf.io.gfile.makedirs(os.path.dirname(iou_path))
  
          collision_path = os.path.join(
              FLAGS.logdir,
              FLAGS.metrics_dir,
              "collision",
              "collision_" + str(FLAGS.part_id).zfill(4) + ".pkl",
          )
          if not tf.io.gfile.exists(os.path.dirname(collision_path)):
              tf.io.gfile.makedirs(os.path.dirname(collision_path))
  
          evaluator = evaluator_util.Evaluator(
              {
                  "3D_IoU": evaluator_util.IoUMetric(
                      max_num_classes=FLAGS.num_classes,
                      slave=slave,
                      path=iou_path,
                      tol=FLAGS.tol,
                  ),
              },
              split=FLAGS.split,
              shapenet_dir=FLAGS.shapenet_dir,
          )
      if FLAGS.francis:
          evaluator = evaluator_util.Evaluator(
              {}, split=FLAGS.split, shapenet_dir=FLAGS.shapenet_dir
          )
      return evaluator
  
imports: import os
  import pickle
  import time
  from absl import app
  from absl import flags
  from absl import logging
  import matplotlib.pyplot
  import numpy
  import tensorflow
  import tensorflow_graphics.projects.points_to_3Dobjects.data_preparation.extract_protos
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import collision_loss
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import cross_entropy_loss
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import focal_loss
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import focal_loss_sparse
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import learning_rate_schedule
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import regression_huber_loss
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import regression_l1_loss
  import tensorflow_graphics.projects.points_to_3Dobjects.models.centernet_vid
  from tensorflow_graphics.projects.points_to_3Dobjects.transforms import transforms_factory
  from tensorflow_graphics.projects.points_to_3Dobjects.utils import evaluator
  import tensorflow_graphics.projects.points_to_3Dobjects.utils.io
  import tensorflow_graphics.projects.points_to_3Dobjects.utils.logger
  import tensorflow_graphics.projects.points_to_3Dobjects.utils.plot
  import tensorflow_graphics.projects.points_to_3Dobjects.utils.tf_utils
  from google3.pyglib import gfile
label_desc: Use image node_id 6 for calling a global function with example usage: get_evaluator() and returns: evaluator