code: def __init__(self, target_dict, embed_tokens, layers, decoder_config):
      super().__init__()
      self.dropout = decoder_config.dropout
  
      input_embed_dim = embed_tokens.embedding_dim
      embed_dim = decoder_config.decoder_embed_dim
      output_embed_dim = decoder_config.decoder_output_dim
  
      padding_idx = target_dict.get_pad_index()
      self.max_target_positions = decoder_config.max_target_positions
  
      self.embed_tokens = embed_tokens
      self.embed_scale = math.sqrt(
          embed_dim
      )  # todo: try with input_embed_dim
      self.padding_idx = padding_idx
  
      self.no_token_positional_embeddings = (
          decoder_config.no_token_positional_embeddings
      )
      # creating this is also conditional
      self.project_in_dim = (
          Linear(input_embed_dim, embed_dim)
          if embed_dim != input_embed_dim
          else PlaceholderIdentity()
      )
      self.embed_layer_norm = LayerNorm(embed_dim)
      self.combine_pos_embed = decoder_config.combine_pos_embed.value
      self.embed_positions = build_positional_embedding(
          positional_embedding_type=decoder_config.positional_embedding_type,
          combine_pos_embed=decoder_config.combine_pos_embed,
          max_target_positions=decoder_config.max_target_positions,
          input_embed_dim=input_embed_dim,
          embed_dim=embed_dim,
          padding_idx=padding_idx,
          no_token_positional_embeddings=decoder_config.no_token_positional_embeddings,
      )
  
      self.layers = nn.ModuleList(layers)
  
      self.project_out_dim = (
          Linear(embed_dim, output_embed_dim, bias=False)
          if embed_dim != output_embed_dim
          else PlaceholderIdentity()
      )
  
      self.normalize = decoder_config.decoder_normalize_before
      if self.normalize:
          self.layer_norm = LayerNorm(embed_dim)
      else:
          self.layer_norm = PlaceholderIdentity()
  
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 LightConvDecoderBase object from inherited base classes: PyTextIncrementalDecoderComponent with example: obj = LightConvDecoderBase(target_dict, embed_tokens, layers, decoder_config)