code: def __init__(self, config: Config, *args, **kwargs):
      super().__init__(config, *args, **kwargs)
      self.refiner_head = Refiner(self.config.refiner_config)
      self.mlp_loss_head = MLPWithLoss(self.config.mlp_loss_config)
      self.max_sample_size = self.config.mlp_loss_config.max_sample_size
      self.msloss_weight = self.config.msloss_weight
      if self.config.num_labels > 2:
          self.is_multilabel = True
      else:
          self.is_multilabel = False
  
      if self.config.use_msloss:
          self.msloss = MSLoss(is_multilabel=self.is_multilabel)
      else:
          self.msloss = None
      self.emb_f = self.config.embedding_key
  
file_path: mmf/mmf/models/transformers/heads/refnet_classifier.py
imports: import logging
  from dataclasses import dataclass
  from typing import Dict, List, Optional
  import torch
  import torch.nn
  from mmf.common.registry import registry
  from mmf.models.transformers.base import BaseTransformerHead
  from mmf.modules.losses import LogitBinaryCrossEntropy, MSLoss
  from .mlp import MLP
  from .refiner import Refiner
label_desc: Use image node_id 1 to create a new RefinerClassifier object from inherited base classes: BaseTransformerHead with example: obj = RefinerClassifier(config)