code: def decoder_input(self, x, mask_info):
      if (
          not self.modality_cfg.transformer_decoder
          or not self.modality_cfg.enc_dec_transformer
      ):
          return super().decoder_input(x, mask_info)
  
      inp_drop = self.modality_cfg.decoder.input_dropout
      if inp_drop > 0:
          x = F.dropout(
              x, inp_drop, training=self.training, inplace=True
          )
  
      kv = x[:, self.modality_cfg.num_extra_tokens :]
  
      assert self.fixed_positional_encoder is not None
      pos = self.fixed_positional_encoder(x, None).expand(
          x.size(0), -1, -1
      )
  
      mask = mask_info.mask.bool()
      if self.modality_cfg.decoder.add_positions_all:
          kv = kv + pos[~mask].view(kv.shape)
  
      q = pos[mask].view(x.size(0), -1, x.size(-1))
  
      return q, kv
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from functools import partial
  from dataclasses import dataclass
  from typing import Callable, Dict, Optional
  from timm.models.layers import to_2tuple
  from fairseq.tasks import FairseqTask
  from examples.data2vec.models.mae import get_2d_sincos_pos_embed, PatchEmbed
  from .base import D2vModalityConfig, ModalitySpecificEncoder, get_alibi_bias, MaskSeed
  from .modules import BlockEncoder, Decoder2d, FixedPositionalEncoder, TransformerDecoder, EncDecTransformerDecoder
  from examples.data2vec.data.modality import Modality
label_desc: Use image node_id 6 for calling the ImageEncoder obj's underlying member method code with example usage: obj.decoder_input(x, mask_info) and returns: q,  kv, super