code: def get_student_backbone_and_num_features(
      config, pre_process=True, post_process=True
  ):
      args = get_args()
  
      if args.vision_backbone_type == "vit":
          student = VitBackbone(
              config,
              pre_process=pre_process,
              post_process=post_process,
              drop_path_rate=0.1,
              single_token_output=True,
          )
          num_features = args.hidden_size
      elif args.vision_backbone_type == "mit":
          student = mit_b5_avg(drop_path_rate=0.1)
          num_features = 512
      elif args.vision_backbone_type == "swin":
          student = get_swin()
          num_features = student.num_features
      else:
          raise Exception(
              "{} vision backbone is not supported.".format(
                  args.vision_backbone_type
              )
          )
  
      return student, num_features
  
imports: import math
  import apex
  import einops
  import torch
  import numpy
  import torch.nn.functional
  from torch.nn.init import trunc_normal_
  from megatron import get_args, print_rank_0
  from megatron.model.utils import get_linear_layer
  from megatron.model.vision.vit_backbone import VitBackbone
  from megatron.model.module import MegatronModule
  from megatron.model.vision.mit_backbone import mit_b5_avg
  from megatron.model.vision.esvit_swin_backbone import get_swin
label_desc: Use image node_id 2 for calling a global function with example usage: get_student_backbone_and_num_features(config, pre_process, post_process) and returns: student,  num_features