class_docstr: We use the "DefaultTrainer" which contains a number pre-defined logic for
  standard training workflow. They may not work for you, especially if you
  are working on a new research project. In that case you can use the cleaner
  "SimpleTrainer", or write your own training loop.
docstr: It now calls :func:`detectron2.solver.build_lr_scheduler`.
  Overwrite it if you'd like a different scheduler.
docstr_tok: ["It","now","calls",":","func",":","`","detectron2.solver.build_lr_scheduler","`",".","Overwrite","it","if","you","'d","like","a","different","scheduler","."]
code: def build_lr_scheduler(cls, cfg, optimizer):
      """
      It now calls :func:`detectron2.solver.build_lr_scheduler`.
      Overwrite it if you'd like a different scheduler.
      """
      return build_lr_scheduler(cfg, optimizer)
  
file_path: detectron2/projects/DeepLab/train_net.py
imports: import os
  import detectron2.data.transforms
  from detectron2.checkpoint import DetectionCheckpointer
  from detectron2.config import get_cfg
  from detectron2.data import DatasetMapper, MetadataCatalog, build_detection_train_loader
  from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
  from detectron2.evaluation import CityscapesSemSegEvaluator, DatasetEvaluators, SemSegEvaluator
  from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
label_desc: Use image node_id 3 for calling the Trainer obj's underlying member method code with example usage: obj.build_lr_scheduler(cls, cfg, optimizer) and returns: build_lr_scheduler