code: def __init__(
      self,
      image_size=224,
      spectrogram_length=2048,
      frequency_length=128,
      image_patch_size=[16, 16],
      audio_patch_size=[16, 16],
      num_image_channels=3,
      num_audio_channels=1,
      num_frames=8,
      hidden_size=768,
      num_hidden_layers=12,
      num_attention_heads=12,
      intermediate_size=3072,
      hidden_act="gelu",
      hidden_dropout_prob=0.0,
      attention_probs_dropout_prob=0.0,
      initializer_range=0.02,
      layer_norm_eps=1e-6,
      qkv_bias=True,
      use_mean_pooling=False,
      decoder_num_attention_heads=16,
      decoder_hidden_size=512,
      decoder_num_hidden_layers=8,
      decoder_intermediate_size=2048,
      pixel_mask_ratio=0.75,
      audio_mask_ratio=0.15,
      audio_mask_type="frame-level",
      task_matching=True,
      task_mae=True,
      loss_type="classification",
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      if audio_mask_type not in ("frame-level", "patch_level"):
          raise ValueError(
              "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
              f"got {audio_mask_type}"
          )
  
      self.image_size = image_size
      self.spectrogram_length = spectrogram_length
      self.frequency_length = frequency_length
      self.image_patch_size = image_patch_size
      self.audio_patch_size = audio_patch_size
      self.num_image_channels = num_image_channels
      self.num_audio_channels = num_audio_channels
      self.num_frames = num_frames
  
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.intermediate_size = intermediate_size
      self.hidden_act = hidden_act
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.qkv_bias = qkv_bias
      self.use_mean_pooling = use_mean_pooling
  
      self.decoder_num_attention_heads = decoder_num_attention_heads
      self.decoder_hidden_size = decoder_hidden_size
      self.decoder_num_hidden_layers = decoder_num_hidden_layers
      self.decoder_intermediate_size = decoder_intermediate_size
      self.pixel_mask_ratio = pixel_mask_ratio
      self.audio_mask_ratio = audio_mask_ratio
      self.audio_mask_type = audio_mask_type
  
      self.task_matching = task_matching
      self.task_mae = task_mae
      self.loss_type = loss_type
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new TvltConfig object from inherited base classes: PretrainedConfig with example: obj = TvltConfig(image_size, spectrogram_length, frequency_length, image_patch_size, audio_patch_size, num_image_channels, num_audio_channels, num_frames, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, initializer_range, layer_norm_eps, qkv_bias, use_mean_pooling, decoder_num_attention_heads, decoder_hidden_size, decoder_num_hidden_layers, decoder_intermediate_size, pixel_mask_ratio, audio_mask_ratio, audio_mask_type, task_matching, task_mae, loss_type)