code: def forward(
      self,
      prev_output_tokens: Tensor,
      encoder_out: Dict[str, Tensor],
      incremental_state: Optional[Dict[str, Tensor]] = None,
      timestep: Optional[int] = None,
  ) -> Tuple[Tensor, Dict[str, Tensor]]:
      hidden_decoder_output = self.forward_unprojected(
          prev_output_tokens, encoder_out, incremental_state, timestep
      )
      return self.projection_layer(
          encoder_out=encoder_out,
          decoder_out=hidden_decoder_output,
          incremental_state=incremental_state,
      )
  
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 for calling the LightConvDecoder obj's underlying member method code with example usage: obj.forward(prev_output_tokens, encoder_out, incremental_state, timestep) and returns: self