code: def __init__(
      self,
      vocab_size=38,
      hidden_size=192,
      num_hidden_layers=6,
      num_attention_heads=2,
      window_size=4,
      use_bias=True,
      ffn_dim=768,
      layerdrop=0.1,
      ffn_kernel_size=3,
      flow_size=192,
      spectrogram_bins=513,
      hidden_act="relu",
      hidden_dropout=0.1,
      attention_dropout=0.1,
      activation_dropout=0.1,
      initializer_range=0.02,
      layer_norm_eps=1e-5,
      use_stochastic_duration_prediction=True,
      num_speakers=1,
      speaker_embedding_size=0,
      upsample_initial_channel=512,
      upsample_rates=[8, 8, 2, 2],
      upsample_kernel_sizes=[16, 16, 4, 4],
      resblock_kernel_sizes=[3, 7, 11],
      resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
      leaky_relu_slope=0.1,
      depth_separable_channels=2,
      depth_separable_num_layers=3,
      duration_predictor_flow_bins=10,
      duration_predictor_tail_bound=5.0,
      duration_predictor_kernel_size=3,
      duration_predictor_dropout=0.5,
      duration_predictor_num_flows=4,
      duration_predictor_filter_channels=256,
      prior_encoder_num_flows=4,
      prior_encoder_num_wavenet_layers=4,
      posterior_encoder_num_wavenet_layers=16,
      wavenet_kernel_size=5,
      wavenet_dilation_rate=1,
      wavenet_dropout=0.0,
      speaking_rate=1.0,
      noise_scale=0.667,
      noise_scale_duration=0.8,
      sampling_rate=16_000,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.window_size = window_size
      self.use_bias = use_bias
      self.ffn_dim = ffn_dim
      self.layerdrop = layerdrop
      self.ffn_kernel_size = ffn_kernel_size
      self.flow_size = flow_size
      self.spectrogram_bins = spectrogram_bins
      self.hidden_act = hidden_act
      self.hidden_dropout = hidden_dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.use_stochastic_duration_prediction = (
          use_stochastic_duration_prediction
      )
      self.num_speakers = num_speakers
      self.speaker_embedding_size = speaker_embedding_size
      self.upsample_initial_channel = upsample_initial_channel
      self.upsample_rates = upsample_rates
      self.upsample_kernel_sizes = upsample_kernel_sizes
      self.resblock_kernel_sizes = resblock_kernel_sizes
      self.resblock_dilation_sizes = resblock_dilation_sizes
      self.leaky_relu_slope = leaky_relu_slope
      self.depth_separable_channels = depth_separable_channels
      self.depth_separable_num_layers = depth_separable_num_layers
      self.duration_predictor_flow_bins = duration_predictor_flow_bins
      self.duration_predictor_tail_bound = duration_predictor_tail_bound
      self.duration_predictor_kernel_size = (
          duration_predictor_kernel_size
      )
      self.duration_predictor_dropout = duration_predictor_dropout
      self.duration_predictor_num_flows = duration_predictor_num_flows
      self.duration_predictor_filter_channels = (
          duration_predictor_filter_channels
      )
      self.prior_encoder_num_flows = prior_encoder_num_flows
      self.prior_encoder_num_wavenet_layers = (
          prior_encoder_num_wavenet_layers
      )
      self.posterior_encoder_num_wavenet_layers = (
          posterior_encoder_num_wavenet_layers
      )
      self.wavenet_kernel_size = wavenet_kernel_size
      self.wavenet_dilation_rate = wavenet_dilation_rate
      self.wavenet_dropout = wavenet_dropout
      self.speaking_rate = speaking_rate
      self.noise_scale = noise_scale
      self.noise_scale_duration = noise_scale_duration
      self.sampling_rate = sampling_rate
  
      if len(upsample_kernel_sizes) != len(upsample_rates):
          raise ValueError(
              f"The length of `upsample_kernel_sizes` ({len(upsample_kernel_sizes)}) must match the length of "
              f"`upsample_rates` ({len(upsample_rates)})"
          )
  
      super().__init__(**kwargs)
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new VitsConfig object from inherited base classes: PretrainedConfig with example: obj = VitsConfig(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, window_size, use_bias, ffn_dim, layerdrop, ffn_kernel_size, flow_size, spectrogram_bins, hidden_act, hidden_dropout, attention_dropout, activation_dropout, initializer_range, layer_norm_eps, use_stochastic_duration_prediction, num_speakers, speaker_embedding_size, upsample_initial_channel, upsample_rates, upsample_kernel_sizes, resblock_kernel_sizes, resblock_dilation_sizes, leaky_relu_slope, depth_separable_channels, depth_separable_num_layers, duration_predictor_flow_bins, duration_predictor_tail_bound, duration_predictor_kernel_size, duration_predictor_dropout, duration_predictor_num_flows, duration_predictor_filter_channels, prior_encoder_num_flows, prior_encoder_num_wavenet_layers, posterior_encoder_num_wavenet_layers, wavenet_kernel_size, wavenet_dilation_rate, wavenet_dropout, speaking_rate, noise_scale, noise_scale_duration, sampling_rate)