code: def compute_mask(
      self,
      x,
      padding_mask,
      mask_seed: Optional[MaskSeed],
      apply,
      shape=None,
      precomputed_mask=None,
  ):
      mlen = self.modality_cfg.mask_length
      if mlen <= 1:
          return super().compute_mask(
              x, padding_mask, mask_seed, apply, precomputed_mask
          )
  
      if precomputed_mask is not None:
          mask = precomputed_mask
      else:
          from fairseq.data.data_utils import compute_block_mask_2d
  
          if shape is not None:
              B, L, D = shape
          else:
              B, L, D = x.shape
  
          mask = compute_block_mask_2d(
              shape=(B, L),
              mask_prob=self.modality_cfg.mask_prob,
              mask_length=self.modality_cfg.mask_length,
              mask_prob_adjust=self.modality_cfg.mask_prob_adjust,
              inverse_mask=self.modality_cfg.inverse_mask,
              require_same_masks=True,
              mask_dropout=self.modality_cfg.mask_dropout,
          )
  
      mask_info = self.make_maskinfo(x, mask, shape)
      if apply:
          x = self.apply_mask(x, mask_info)
  
      return x, mask_info
  
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 5 for calling the ImageEncoder obj's underlying member method code with example usage: obj.compute_mask(x, padding_mask, mask_seed, apply, shape, precomputed_mask) and returns: x,  mask_info, super