code: def test_opt_sequence_classification_model(self):
      config, input_dict = self.model_tester.prepare_config_and_inputs()
      config.num_labels = 3
      input_ids = input_dict["input_ids"]
      attention_mask = input_ids.ne(1).to(torch_device)
      sequence_labels = ids_tensor(
          [self.model_tester.batch_size],
          self.model_tester.type_sequence_label_size,
      )
      model = OPTForSequenceClassification(config)
      model.to(torch_device)
      model.eval()
      result = model(
          input_ids,
          attention_mask=attention_mask,
          labels=sequence_labels,
      )
      self.assertEqual(
          result.logits.shape,
          (self.model_tester.batch_size, self.model_tester.num_labels),
      )
  
imports: import copy
  import tempfile
  import unittest
  import timeout_decorator
  from transformers import OPTConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  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 7 for calling the OPTModelTest obj's underlying member method code with example usage: obj.test_opt_sequence_classification_model() without return types