code: def test_forward(self):
      module = RefinerClassifier(self.config)
      sequence_input = torch.ones(size=(5, 128, 768), dtype=torch.float)
      encoder_output = [sequence_input, sequence_input]
      processed_sample_list = {}
      processed_sample_list["masks"] = {}
      processed_sample_list["masks"]["text"] = torch.ones(
          size=(5, 64), dtype=torch.long
      )
      processed_sample_list["masks"]["image"] = torch.ones(
          size=(5, 64), dtype=torch.long
      )
      processed_sample_list["target_key"] = {}
      processed_sample_list["target_key"]["targets"] = torch.empty(
          5, dtype=torch.long
      ).random_(2)
      output = module(
          sequence_input, encoder_output, processed_sample_list
      )
      self.assertTrue("losses" in output)
      self.assertTrue("fused_embedding" in output)
      self.assertTrue("ms_loss" in output["losses"].keys())
  
imports: import unittest
  import torch
  from mmf.common.sample import Sample
  from mmf.models.transformers.heads.contrastive import ThreeWayContrastive
  from mmf.models.transformers.heads.itm import ITM
  from mmf.models.transformers.heads.mlm import MLM
  from mmf.models.transformers.heads.mlp import MLP
  from mmf.models.transformers.heads.mrc import MRC
  from mmf.models.transformers.heads.mrfr import MRFR
  from mmf.models.transformers.heads.refiner import Refiner
  from mmf.models.transformers.heads.refnet_classifier import RefinerClassifier
  from mmf.models.transformers.heads.wra import WRA
  from omegaconf import OmegaConf
  from tests.test_utils import skip_if_no_cuda
  from torch import nn
label_desc: Use image node_id 2 for calling the TestRefNetClassifierHead obj's underlying member method code with example usage: obj.test_forward() without return types