code: def _test_inference_with_d_vector(self, batch_size):
      (
          input_dummy,
          input_lengths,
          mel_spec,
          mel_lengths,
          speaker_ids,
      ) = self._create_inputs(batch_size)
      d_vector = torch.rand(batch_size, 256).to(device)
      config = GlowTTSConfig(
          num_chars=32,
          use_d_vector_file=True,
          d_vector_dim=256,
          d_vector_file=os.path.join(
              get_tests_data_path(), "dummy_speakers.json"
          ),
      )
      model = GlowTTS.init_from_config(config, verbose=False).to(device)
      model.eval()
      outputs = model.inference(
          input_dummy,
          {"x_lengths": input_lengths, "d_vectors": d_vector},
      )
      self._assert_inference_outputs(outputs, input_dummy, mel_spec)
  
imports: import copy
  import os
  import unittest
  import torch
  from torch import optim
  from trainer.logging.tensorboard_logger import TensorboardLogger
  from tests import get_tests_data_path, get_tests_input_path, get_tests_output_path
  from TTS.tts.configs.glow_tts_config import GlowTTSConfig
  from TTS.tts.layers.losses import GlowTTSLoss
  from TTS.tts.models.glow_tts import GlowTTS
  from TTS.tts.utils.speakers import SpeakerManager
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 15 for calling the TestGlowTTS obj's underlying member method code with example usage: obj._test_inference_with_d_vector(batch_size) without return types