code: def get_dataset(name, image_set, transform, data_path):
      paths = {
          "coco": (data_path, get_coco, 91),
          "coco_kp": (data_path, get_coco_kp, 2),
      }
      p, ds_fn, num_classes = paths[name]
  
      ds = ds_fn(p, image_set=image_set, transforms=transform)
      return ds, num_classes
  
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 1 for calling a global function with example usage: get_dataset(name, image_set, transform, data_path) and returns: ds,  num_classes