class_docstr: Base `vc` class. Every new `vc` model must inherit this.
  
  It defines common `vc` specific functions on top of `Model` implementation.
class_docstr_tok: ["Base","`","vc","`","class",".","Every","new","`","vc","`","model","must","inherit","this",".","It","defines","common","`","vc","`","specific","functions","on","top","of","`","Model","`","implementation","."]
code: def _get_test_aux_input(
      self,
  ) -> Dict:
      d_vector = None
      if self.config.use_d_vector_file:
          d_vector = [
              self.speaker_manager.embeddings[name]["embedding"]
              for name in self.speaker_manager.embeddings
          ]
          d_vector = (random.sample(sorted(d_vector), 1),)
  
      aux_inputs = {
          "speaker_id": None
          if not self.config.use_speaker_embedding
          else random.sample(
              sorted(self.speaker_manager.name_to_id.values()), 1
          ),
          "d_vector": d_vector,
          "style_wav": None,  # TODO: handle GST style input
      }
      return aux_inputs
  
imports: import os
  import random
  from typing import Dict, List, Tuple, Union
  import torch
  import torch.distributed
  from coqpit import Coqpit
  from torch import nn
  from torch.utils.data import DataLoader
  from torch.utils.data.sampler import WeightedRandomSampler
  from trainer.torch import DistributedSampler, DistributedSamplerWrapper
  from TTS.model import BaseTrainerModel
  from TTS.tts.datasets.dataset import TTSDataset
  from TTS.tts.utils.data import get_length_balancer_weights
  from TTS.tts.utils.languages import LanguageManager, get_language_balancer_weights
  from TTS.tts.utils.speakers import SpeakerManager, get_speaker_balancer_weights
  from TTS.tts.utils.synthesis import synthesis
  from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
label_desc: Use image node_id 9 for calling the BaseVC obj's underlying member method code with example usage: obj._get_test_aux_input() and returns: aux_inputs