code: def _vanilla_transformer(self, x, src_tokens):
      x += self.embed_positions(src_tokens)
      return x
  
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 5 for calling the LightConvDecoderBase obj's underlying member method code with example usage: obj._vanilla_transformer(x, src_tokens) and returns: x