code: def __init__(
      self,
      vocab_size=10000,
      encoder_layers=12,
      encoder_ffn_dim=2048,
      encoder_attention_heads=4,
      decoder_layers=6,
      decoder_ffn_dim=2048,
      decoder_attention_heads=4,
      encoder_layerdrop=0.0,
      decoder_layerdrop=0.0,
      use_cache=True,
      is_encoder_decoder=True,
      activation_function="relu",
      d_model=256,
      dropout=0.1,
      attention_dropout=0.0,
      activation_dropout=0.0,
      init_std=0.02,
      decoder_start_token_id=2,
      scale_embedding=True,
      pad_token_id=1,
      bos_token_id=0,
      eos_token_id=2,
      max_source_positions=6000,
      max_target_positions=1024,
      num_conv_layers=2,
      conv_kernel_sizes=(5, 5),
      conv_channels=1024,
      input_feat_per_channel=80,
      input_channels=1,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.d_model = d_model
      self.encoder_ffn_dim = encoder_ffn_dim
      self.encoder_layers = encoder_layers
      self.encoder_attention_heads = encoder_attention_heads
      self.decoder_ffn_dim = decoder_ffn_dim
      self.decoder_layers = decoder_layers
      self.decoder_attention_heads = decoder_attention_heads
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.activation_function = activation_function
      self.init_std = init_std
      self.encoder_layerdrop = encoder_layerdrop
      self.decoder_layerdrop = decoder_layerdrop
      self.use_cache = use_cache
      self.num_hidden_layers = encoder_layers
      self.scale_embedding = (
          scale_embedding  # scale factor will be sqrt(d_model) if True
      )
      self.max_source_positions = max_source_positions
      self.max_target_positions = max_target_positions
      self.num_conv_layers = num_conv_layers
      self.conv_kernel_sizes = list(conv_kernel_sizes)
      self.conv_channels = conv_channels
      self.input_feat_per_channel = input_feat_per_channel
      self.input_channels = input_channels
  
      if len(self.conv_kernel_sizes) != self.num_conv_layers:
          raise ValueError(
              "Configuration for convolutional module is incorrect. "
              "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` "
              f"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, "
              f"`config.num_conv_layers = {self.num_conv_layers}`."
          )
  
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          is_encoder_decoder=is_encoder_decoder,
          decoder_start_token_id=decoder_start_token_id,
          **kwargs,
      )
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new Speech2TextConfig object from inherited base classes: PretrainedConfig with example: obj = Speech2TextConfig(vocab_size, encoder_layers, encoder_ffn_dim, encoder_attention_heads, decoder_layers, decoder_ffn_dim, decoder_attention_heads, encoder_layerdrop, decoder_layerdrop, use_cache, is_encoder_decoder, activation_function, d_model, dropout, attention_dropout, activation_dropout, init_std, decoder_start_token_id, scale_embedding, pad_token_id, bos_token_id, eos_token_id, max_source_positions, max_target_positions, num_conv_layers, conv_kernel_sizes, conv_channels, input_feat_per_channel, input_channels)