code: def create_and_check_model(self, config, input_ids, *args):
      model = FastSpeech2ConformerModel(config=config)
      model.to(torch_device)
      model.eval()
      result = model(input_ids, return_dict=True)
  
      # total of 5 keys in result
      self.parent.assertEqual(len(result), 5)
      # check batch sizes match
      for value in result.values():
          self.parent.assertEqual(value.size(0), self.batch_size)
      # check duration, pitch, and energy have the appopriate shapes
      # duration: (batch_size, max_text_length), pitch and energy: (batch_size, max_text_length, 1)
      self.parent.assertEqual(
          result["duration_outputs"].shape + (1,),
          result["pitch_outputs"].shape,
      )
      self.parent.assertEqual(
          result["pitch_outputs"].shape, result["energy_outputs"].shape
      )
      # check predicted mel-spectrogram has correct dimension
      self.parent.assertEqual(
          result["spectrogram"].size(2), model.config.num_mel_bins
      )
  
imports: import inspect
  import tempfile
  import unittest
  from transformers import FastSpeech2ConformerConfig, FastSpeech2ConformerHifiGanConfig, FastSpeech2ConformerTokenizer, FastSpeech2ConformerWithHifiGanConfig, is_torch_available
  from transformers.testing_utils import require_g2p_en, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor
label_desc: Use image node_id 4 for calling the FastSpeech2ConformerModelTester obj's underlying member method code with example usage: obj.create_and_check_model(config, input_ids) without return types