code: def test_vqa_forward(self):
      model = load_model("albef_vqa", model_type="vqav2", is_eval=True)
      samples = {
          "image": torch.rand(2, 3, 384, 384),
          "text_input": ["What is this?", "What is that?"],
          "answer": ["cat", "cat", "dog"],
          "weight": torch.tensor([1.0, 1.0, 1.0]),
          "n_answers": torch.tensor([2, 1]),
          "epoch": 0,
          "iters": 0,
          "num_iters_per_epoch": 1000,
      }
      output = model(samples)
      # odict_keys(['image_embeds', 'image_embeds_m', 'encoder_output', 'encoder_output_m', 'decoder_output', 'decoder_labels'])
  
      assert (
          output.intermediate_output.image_embeds.shape
          == torch.Size([2, 577, 768])
      )
      assert (
          output.intermediate_output.image_embeds_m.shape
          == torch.Size([2, 577, 768])
      )
  
      assert (
          output.intermediate_output.encoder_output.last_hidden_state.shape
          == torch.Size([2, 6, 768])
      )
      assert (
          output.intermediate_output.encoder_output_m.last_hidden_state.shape
          == torch.Size([2, 6, 768])
      )
  
      assert (
          output.intermediate_output.decoder_output.logits.shape
          == torch.Size([3, 3, 30522])
      )
      assert (
          output.intermediate_output.decoder_labels.shape
          == torch.Size([3, 3])
      )
  
file_path: lavis/tests/models/test_albef.py
imports: import pytest
  import torch
  from lavis.models import load_model, load_model_and_preprocess
  from PIL import Image
label_desc: Use image node_id 2 for calling the TestAlbef obj's underlying member method code with example usage: obj.test_vqa_forward() without return types