code: def get_task_type(self, dataset_name):
      task_type = "detection"
      if dataset_name in self.config.heads["vl"]:
          task_type = "vl"
      elif dataset_name in self.config.heads["glue"]:
          task_type = "glue"
      return task_type
  
imports: import logging
  from copy import deepcopy
  from typing import Dict
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.models.unit.unit_base_model import AttributeHead, build_detection_loss, MLP, UniTBaseModel
  from mmf.modules.encoders import TransformerEncoder
  from mmf.utils.distributed import byte_tensor_to_object
  from torch import nn, Tensor
label_desc: Use image node_id 11 for calling the UniT obj's underlying member method code with example usage: obj.get_task_type(dataset_name) and returns: task_type