code: def check_seq_classifier_training(self, config, input_values, *args):
      config.ctc_zero_infinity = True
      model = SEWDForSequenceClassification(config=config)
      model.to(torch_device)
      model.train()
  
      # freeze everything but the classification head
      model.freeze_base_model()
  
      input_values = input_values[:3]
  
      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
  
      loss = model(input_values, labels=labels).loss
      self.parent.assertFalse(torch.isinf(loss).item())
  
      loss.backward()
  
imports: import math
  import unittest
  import pytest
  from transformers import SEWDConfig, 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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 9 for calling the SEWDModelTester obj's underlying member method code with example usage: obj.check_seq_classifier_training(config, input_values) without return types