code: def test_d_vector_inference(self):
      args = VitsArgs(
          spec_segment_size=10,
          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")
          ],
      )
      config = VitsConfig(model_args=args)
      model = Vits.init_from_config(config, verbose=False).to(device)
      model.eval()
      # batch size = 1
      input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
      d_vectors = torch.randn(1, 256).to(device)
      outputs = model.inference(
          input_dummy, aux_input={"d_vectors": d_vectors}
      )
      self._check_inference_outputs(config, outputs, input_dummy)
      # batch size = 2
      input_dummy, input_lengths, *_ = self._create_inputs(config)
      d_vectors = torch.randn(2, 256).to(device)
      outputs = model.inference(
          input_dummy,
          aux_input={
              "x_lengths": input_lengths,
              "d_vectors": d_vectors,
          },
      )
      self._check_inference_outputs(
          config, outputs, input_dummy, batch_size=2
      )
  
imports: import copy
  import os
  import unittest
  import torch
  from trainer.logging.tensorboard_logger import TensorboardLogger
  from tests import assertHasAttr, assertHasNotAttr, get_tests_data_path, get_tests_input_path, get_tests_output_path
  from TTS.config import load_config
  from TTS.encoder.utils.generic_utils import setup_encoder_model
  from TTS.tts.configs.vits_config import VitsConfig
  from TTS.tts.models.vits import Vits, VitsArgs, VitsAudioConfig, amp_to_db, db_to_amp, load_audio, spec_to_mel, wav_to_mel, wav_to_spec
  from TTS.tts.utils.speakers import SpeakerManager
label_desc: Use image node_id 18 for calling the TestVits obj's underlying member method code with example usage: obj.test_d_vector_inference() without return types