code: def test_model(self):
      tokenizer = AutoTokenizer.from_pretrained(self.text_model)
      max_length = 80
      caption = "Hajnówka [SEP] loka monumento pri bizono"
      caption_inputs = tokenizer.encode_plus(
          caption,
          truncation=True,
          add_special_tokens=True,
          max_length=max_length,
          padding="max_length",
      )
      caption_ids = caption_inputs["input_ids"]
      caption_mask = caption_inputs["attention_mask"]
      url = "Zubr-Hajnowka"
      url_inputs = tokenizer.encode_plus(
          url,
          truncation=True,
          add_special_tokens=True,
          max_length=max_length,
          padding="max_length",
      )
      url_ids = url_inputs["input_ids"]
      url_mask = url_inputs["attention_mask"]
      url_ids = torch.tensor([url_ids], dtype=torch.long).to(
          self.device
      )
      url_mask = torch.tensor([url_mask], dtype=torch.long).to(
          self.device
      )
      caption_ids = torch.tensor([caption_ids], dtype=torch.long).to(
          self.device
      )
      caption_mask = torch.tensor([caption_mask], dtype=torch.long).to(
          self.device
      )
      dummy_img = torch.rand(1, 3, 224, 224).to(self.device)
      (
          query_feats,
          caption_feats,
          alphas,
      ) = self.matcher.compute_embeddings(
          img=dummy_img,
          url=url_ids,
          url_mask=url_mask,
          caption=caption_ids,
          caption_mask=caption_mask,
      )
      self.assertEqual(query_feats.shape, (1, 1024))
      self.assertEqual(caption_feats.shape, (1, 1024))
      self.assertEqual(alphas.shape, (1, 2))
  
imports: import unittest
  import torch
  from transformers import AutoTokenizer
  from towhee.models.mcprop.matching import Matching
label_desc: Use image node_id 1 for calling the MatchingTest obj's underlying member method code with example usage: obj.test_model() without return types