code: def check_seq_classifier_loss(self, config, input_values, *args):
      model = SEWForSequenceClassification(config=config)
      model.to(torch_device)
  
      # make sure that dropout is disabled
      model.eval()
  
      input_values = input_values[:3]
      attention_mask = torch.ones(
          input_values.shape, device=torch_device, dtype=torch.long
      )
  
      input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
      labels = ids_tensor(
          (input_values.shape[0], 1), len(model.config.id2label)
      )
  
      # pad input
      for i in range(len(input_lengths)):
          input_values[i, input_lengths[i] :] = 0.0
          attention_mask[i, input_lengths[i] :] = 0
  
      masked_loss = model(
          input_values, attention_mask=attention_mask, labels=labels
      ).loss.item()
      unmasked_loss = model(input_values, labels=labels).loss.item()
  
      self.parent.assertTrue(isinstance(masked_loss, float))
      self.parent.assertTrue(isinstance(unmasked_loss, float))
      self.parent.assertTrue(masked_loss != unmasked_loss)
  
imports: import math
  import unittest
  import pytest
  from transformers import SEWConfig, is_torch_available
  from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 8 for calling the SEWModelTester obj's underlying member method code with example usage: obj.check_seq_classifier_loss(config, input_values) without return types