code: def __init__(
      self,
      vocab_size: int,
      encoder_output_size: int,
      attention_heads: int = 4,
      linear_units: int = 2048,
      num_blocks: int = 6,
      dropout_rate: float = 0.1,
      positional_dropout_rate: float = 0.1,
      self_attention_dropout_rate: float = 0.0,
      src_attention_dropout_rate: float = 0.0,
      input_layer: str = "embed",
      use_output_layer: bool = True,
      pos_enc_class=PositionalEncoding,
      normalize_before: bool = True,
      concat_after: bool = False,
      conv_wshare: int = 4,
      conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
      conv_usebias: int = False,
  ):
      assert check_argument_types()
      if len(conv_kernel_length) != num_blocks:
          raise ValueError(
              "conv_kernel_length must have equal number of values to num_blocks: "
              f"{len(conv_kernel_length)} != {num_blocks}"
          )
      super().__init__(
          vocab_size=vocab_size,
          encoder_output_size=encoder_output_size,
          dropout_rate=dropout_rate,
          positional_dropout_rate=positional_dropout_rate,
          input_layer=input_layer,
          use_output_layer=use_output_layer,
          pos_enc_class=pos_enc_class,
          normalize_before=normalize_before,
      )
  
      attention_dim = encoder_output_size
      self.decoders = repeat(
          num_blocks,
          lambda lnum: DecoderLayer(
              attention_dim,
              LightweightConvolution(
                  wshare=conv_wshare,
                  n_feat=attention_dim,
                  dropout_rate=self_attention_dropout_rate,
                  kernel_size=conv_kernel_length[lnum],
                  use_kernel_mask=True,
                  use_bias=conv_usebias,
              ),
              MultiHeadedAttention(
                  attention_heads,
                  attention_dim,
                  src_attention_dropout_rate,
              ),
              PositionwiseFeedForward(
                  attention_dim, linear_units, dropout_rate
              ),
              dropout_rate,
              normalize_before,
              concat_after,
          ),
      )
  
imports: from typing import Any, List, Sequence, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder_layer import DecoderLayer
  from espnet.nets.pytorch_backend.transformer.dynamic_conv import DynamicConvolution
  from espnet.nets.pytorch_backend.transformer.dynamic_conv2d import DynamicConvolution2D
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
  from espnet.nets.pytorch_backend.transformer.lightconv import LightweightConvolution
  from espnet.nets.pytorch_backend.transformer.lightconv2d import LightweightConvolution2D
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
  from espnet.nets.pytorch_backend.transformer.repeat import repeat
  from espnet.nets.scorer_interface import BatchScorerInterface
label_desc: Use image node_id 1 to create a new LightweightConvolutionTransformerDecoder object from inherited base classes: BaseTransformerDecoder with example: obj = LightweightConvolutionTransformerDecoder(vocab_size, encoder_output_size, attention_heads, linear_units, num_blocks, dropout_rate, positional_dropout_rate, _attention_dropout_rate, src_attention_dropout_rate, input_layer, use_output_layer, pos_enc_class, normalize_before, concat_after, conv_wshare, conv_kernel_length, conv_usebias)