class_docstr: Defines parameters for VITS End2End TTS model.
  
  Args:
      model (str):
          Model name. Do not change unless you know what you are doing.
  
      model_args (VitsArgs):
          Model architecture arguments. Defaults to `VitsArgs()`.
  
      audio (VitsAudioConfig):
          Audio processing configuration. Defaults to `VitsAudioConfig()`.
  
      grad_clip (List):
          Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
  
      lr_gen (float):
          Initial learning rate for the generator. Defaults to 0.0002.
  
      lr_disc (float):
          Initial learning rate for the discriminator. Defaults to 0.0002.
  
      lr_scheduler_gen (str):
          Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
          `ExponentialLR`.
  
      lr_scheduler_gen_params (dict):
          Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
  
      lr_scheduler_disc (str):
          Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
          `ExponentialLR`.
  
      lr_scheduler_disc_params (dict):
          Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
  
      scheduler_after_epoch (bool):
          If true, step the schedulers after each epoch else after each step. Defaults to `False`.
  
      optimizer (str):
          Name of the optimizer to use with both the generator and the discriminator networks. One of the
          `torch.optim.*`. Defaults to `AdamW`.
  
      kl_loss_alpha (float):
          Loss weight for KL loss. Defaults to 1.0.
  
      disc_loss_alpha (float):
          Loss weight for the discriminator loss. Defaults to 1.0.
  
      gen_loss_alpha (float):
          Loss weight for the generator loss. Defaults to 1.0.
  
      feat_loss_alpha (float):
          Loss weight for the feature matching loss. Defaults to 1.0.
  
      mel_loss_alpha (float):
          Loss weight for the mel loss. Defaults to 45.0.
  
      return_wav (bool):
          If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
  
      compute_linear_spec (bool):
          If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
  
      use_weighted_sampler (bool):
          If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
  
      weighted_sampler_attrs (dict):
          Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
          by overweighting `root_path` by 2.0. Defaults to `{}`.
  
      weighted_sampler_multipliers (dict):
          Weight each unique value of a key returned by the formatter for weighted sampling.
          For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
          It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
  
      r (int):
          Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
  
      add_blank (bool):
          If true, a blank token is added in between every character. Defaults to `True`.
  
      test_sentences (List[List]):
          List of sentences with speaker and language information to be used for testing.
  
      language_ids_file (str):
          Path to the language ids file.
  
      use_language_embedding (bool):
          If true, language embedding is used. Defaults to `False`.
  
  Note:
      Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
  
  Example:
  
      >>> from TTS.tts.configs.vits_config import VitsConfig
      >>> config = VitsConfig()
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code: def __post_init__(self):
      for key, val in self.model_args.items():
          if hasattr(self, key):
              self[key] = val
  
imports: from dataclasses import dataclass, field
  from typing import List
  from TTS.tts.configs.shared_configs import BaseTTSConfig
  from TTS.tts.models.vits import VitsArgs, VitsAudioConfig
label_desc: Use image node_id 1 for calling the VitsConfig obj's underlying member method code with example usage: obj.__post_init__() without return types