code: def __init__(
      self,
      attention_dropout,
      decoder_attention_heads,
      self_attention_heads,
      decoder_conv_dim,
      # ARBABU: need to remove these two type parameters
      decoder_conv_type,
      attention_type,
      self_attention_type,
      decoder_embed_dim,
      decoder_ffn_embed_dim,
      decoder_glu,
      decoder_normalize_before,
      dropout,
      input_dropout,
      relu_dropout,
      need_attention,
      convolution_type,
      conv=None,
      self_attention=None,
      attention=None,
  ):
      super().__init__()
      self.embed_dim = decoder_embed_dim
      self.conv_dim = decoder_conv_dim
      if decoder_glu:
          self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim)
          self.act = nn.GLU()
      else:
          self.linear1 = Linear(self.embed_dim, self.conv_dim)
          self.act = PlaceholderIdentity()
      self.conv = conv
      self.linear2 = Linear(self.conv_dim, self.embed_dim)
  
      self.dropout = dropout
      self.relu_dropout = relu_dropout
      self.input_dropout = input_dropout
      self.normalize_before = decoder_normalize_before
      self.conv_layer_norm = LayerNorm(self.embed_dim)
  
      if attention is None:
          self.no_encoder_attn = True
          self.encoder_attn = PlaceholderAttentionIdentity()
          self.encoder_attn_layer_norm = PlaceholderIdentity()
      else:
          self.no_encoder_attn = False
          self.encoder_attn = attention
          self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
      if self_attention is None:
          self.has_self_attn = False
          self.self_attn = PlaceholderAttentionIdentity()
      else:
          self.has_self_attn = True
          self.self_attn = self_attention
      self.fc1 = Linear(self.embed_dim, decoder_ffn_embed_dim)
      self.fc2 = Linear(decoder_ffn_embed_dim, self.embed_dim)
  
      self.final_layer_norm = LayerNorm(self.embed_dim)
      self.need_attn = need_attention
  
imports: import math
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.config import ConfigBase
  from pytext.config.module_config import ModuleConfig
  from pytext.models.module import create_module
  from pytext.models.seq_models.base import PlaceholderAttentionIdentity
  from pytext.models.seq_models.positional import build_positional_embedding, PostionalEmbedCombine, PostionalEmbedType
  from pytext.models.seq_models.utils import Linear, log_and_overwrite
  from torch import Tensor
  from torch.nn import LayerNorm
  from .attention import MultiheadAttention
  from .base import PlaceholderIdentity, PyTextIncrementalDecoderComponent, PyTextSeq2SeqModule
  from .light_conv import LightweightConv
  from .projection_layers import DecoderWithLinearOutputProjection, DecoupledDecoderHead
  from .utils import extract_ontology_vocab
label_desc: Use image node_id 2 to create a new LightConvDecoderLayer object from inherited base classes: PyTextSeq2SeqModule with example: obj = LightConvDecoderLayer(attention_dropout, decoder_attention_heads, _attention_heads, decoder_conv_dim, decoder_conv_type, attention_type, _attention_type, decoder_embed_dim, decoder_ffn_embed_dim, decoder_glu, decoder_normalize_before, dropout, input_dropout, relu_dropout, need_attention, convolution_type, conv, _attention, attention)