code: def forward_unprojected(
      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]]:
      output_dict: Dict[str, Tensor] = {}
      if incremental_state is not None:
          prev_output_tokens = prev_output_tokens[:, -1:]
  
      # embed tokens and positions
      x = self.embed_scale * self.embed_tokens([[prev_output_tokens]])
  
      if not self.no_token_positional_embeddings:
          # TODO : Verify incremental generation for AR mode
          x = self.pos_embed(x, prev_output_tokens)
      else:
          x = self.project_in_dim(x)
  
      x = self.embed_layer_norm(x)
      x = F.dropout(x, p=self.dropout, training=self.training)
      output_dict["decoder_layer_0"] = x.clone()
  
      # B x T x C -> T x B x C
      x = x.transpose(0, 1)
  
      last_layer_attn: Optional[Tensor] = None
  
      decoder_padding_mask = prev_output_tokens.eq(self.padding_idx)
      target_lengths = (~decoder_padding_mask).sum(dim=1)
  
      if not decoder_padding_mask.any():
          decoder_mask = None
      else:
          decoder_mask = decoder_padding_mask
  
      encoder = encoder_out["encoder_out"]
      encoder_mask: Optional[Tensor] = None
      if "encoder_mask" in encoder_out:
          encoder_mask = encoder_out["encoder_mask"]
  
      # decoder layers
      for idx, layer in enumerate(self.layers):
          encoder = encoder_out["encoder_out"]
          encoder_mask: Optional[Tensor] = None
          if "encoder_mask" in encoder_out:
              encoder_mask = encoder_out["encoder_mask"]
          x, last_layer_attn = layer(
              x, encoder, encoder_mask, decoder_mask, incremental_state
          )
          output_dict["decoder_layer_" + str(idx + 1)] = x.transpose(
              0, 1
          ).clone()
  
      if self.normalize:
          x = self.layer_norm(x)
  
      # T x B x C -> B x T x C
      x = x.transpose(0, 1)
  
      x = self.project_out_dim(x)
  
      if last_layer_attn is not None:
          output_dict["attn_scores"] = last_layer_attn
      output_dict["target_lengths"] = target_lengths
      output_dict["decoder_mask"] = decoder_padding_mask
  
      for key in encoder_out.keys():
          output_dict[key] = encoder_out[key]
  
      return x, output_dict
  
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 3 for calling the LightConvDecoderBase obj's underlying member method code with example usage: obj.forward_unprojected(prev_output_tokens, encoder_out, incremental_state, timestep) and returns: x,  output_dict