code: def create_and_check_batch_inference(
      self, config, input_features, *args
  ):
      # test does not pass for models making use of `group_norm`
      # check: https://github.com/pytorch/fairseq/issues/3227
      model = MCTCTModel(config=config)
      model.to(torch_device)
      model.eval()
  
      input_features = input_features[:3]
      attention_mask = torch.ones(
          input_features.shape[:-1],
          device=torch_device,
          dtype=torch.bool,
      )
  
      input_lengths = [input_features.shape[-1] // i for i in [2, 2, 1]]
  
      # pad input
      for i in range(len(input_lengths)):
          input_features[i, input_lengths[i] :] = 0.0
          attention_mask[i, input_lengths[i] :] = 0.0
  
      batch_outputs = model(
          input_features, attention_mask=attention_mask
      ).last_hidden_state
  
      for i in range(input_features.shape[0]):
          input_slice = input_features[i : i + 1, : input_lengths[i]]
          output = model(input_slice).last_hidden_state
  
          batch_output = batch_outputs[i : i + 1, : output.shape[1]]
          self.parent.assertTrue(
              torch.allclose(output, batch_output, atol=1e-3)
          )
  
imports: import inspect
  import math
  import unittest
  from datasets import load_dataset
  from transformers import MCTCTConfig, 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
label_desc: Use image node_id 6 for calling the MCTCTModelTester obj's underlying member method code with example usage: obj.create_and_check_batch_inference(config, input_features) without return types