code: def test_advanced_inputs(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs()
      config.use_cache = False
      inputs_dict["input_ids"][:, -2:] = config.pad_token_id
      (
          decoder_input_ids,
          decoder_attn_mask,
          causal_mask,
      ) = _prepare_fsmt_decoder_inputs(config, inputs_dict["input_ids"])
      model = FSMTModel(config).to(torch_device).eval()
  
      decoder_features_with_created_mask = model(**inputs_dict)[0]
      decoder_features_with_passed_mask = model(
          decoder_attention_mask=invert_mask(decoder_attn_mask),
          decoder_input_ids=decoder_input_ids,
          **inputs_dict,
      )[0]
      _assert_tensors_equal(
          decoder_features_with_passed_mask,
          decoder_features_with_created_mask,
      )
      useless_mask = torch.zeros_like(decoder_attn_mask)
      decoder_features = model(
          decoder_attention_mask=useless_mask, **inputs_dict
      )[0]
      self.assertTrue(
          isinstance(decoder_features, torch.Tensor)
      )  # no hidden states or attentions
      self.assertEqual(
          decoder_features.size(),
          (
              self.model_tester.batch_size,
              self.model_tester.seq_length,
              config.tgt_vocab_size,
          ),
      )
      if (
          decoder_attn_mask.min().item() < -1e3
      ):  # some tokens were masked
          self.assertFalse(
              (decoder_features_with_created_mask == decoder_features)
              .all()
              .item()
          )
  
      # Test different encoder attention masks
      decoder_features_with_long_encoder_mask = model(
          inputs_dict["input_ids"],
          attention_mask=inputs_dict["attention_mask"].long(),
      )[0]
      _assert_tensors_equal(
          decoder_features_with_long_encoder_mask,
          decoder_features_with_created_mask,
      )
  
imports: import tempfile
  import unittest
  import timeout_decorator
  from parameterized import parameterized
  from transformers import FSMTConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
label_desc: Use image node_id 5 for calling the FSMTModelTest obj's underlying member method code with example usage: obj.test_advanced_inputs() without return types