code: def __init__(self, config: Config, *args, **kwargs):
      super().__init__(config, *args, **kwargs)
      self.loss_name = self.config.loss_name
      if self.config.loss == "cross_entropy":
          self.loss_fn = nn.CrossEntropyLoss()
      elif self.config.loss == "logit_bce":
          self.loss_fn = LogitBinaryCrossEntropy()
      self.init_output_dict = {}
      self.init_output_dict["losses"] = {}
      self.mlp_base = MLP(config)
  
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 MLPWithLoss object from inherited base classes: BaseTransformerHead with example: obj = MLPWithLoss(config)