code: def get_transform(train, args):
      if train:
          return presets.DetectionPresetTrain(args.data_augmentation)
      elif not args.prototype:
          return presets.DetectionPresetEval()
      else:
          if args.weights:
              weights = prototype.models.get_weight(args.weights)
              return weights.transforms()
          else:
              return prototype.transforms.CocoEval()
  
file_path: avalanche/examples/tvdetection/train.py
imports: import datetime
  import os
  import time
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import torchvision.models.detection
  import torchvision.models.detection.mask_rcnn
  import utils
  from coco_utils import get_coco, get_coco_kp
  from engine import train_one_epoch, evaluate
  from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups
label_desc: Use image node_id 2 for calling a global function with example usage: get_transform(train, args) and returns: presets, presets, weights, prototype