code: def convert_padding_mask(self, x, padding_mask):
      if padding_mask is None or padding_mask.size(1) == x.size(1):
          return padding_mask
  
      diff = self.downsample - padding_mask.size(1) % self.downsample
      if 0 < diff < self.downsample:
          padding_mask = F.pad(padding_mask, (0, diff), value=True)
  
      padding_mask = padding_mask.view(
          padding_mask.size(0), -1, self.downsample
      )
      padding_mask = padding_mask.all(-1)
      if padding_mask.size(1) > x.size(1):
          padding_mask = padding_mask[:, : x.size(1)]
  
      assert x.size(1) == padding_mask.size(
          1
      ), f"{x.size(1), padding_mask.size(1), diff, self.downsample}"
  
      return padding_mask
  
imports: import math
  from dataclasses import dataclass
  from functools import partial
  from typing import Callable, Dict, Optional
  import torch.nn
  import torch.nn.functional
  import numpy
  from fairseq.modules import PositionalEmbedding, FairseqDropout, LayerNorm
  from fairseq.tasks import FairseqTask
  from .base import D2vModalityConfig, ModalitySpecificEncoder, get_alibi_bias
  from .modules import BlockEncoder, Decoder1d
  from examples.data2vec.data.modality import Modality
label_desc: Use image node_id 3 for calling the TextEncoder obj's underlying member method code with example usage: obj.convert_padding_mask(x, padding_mask) and returns: padding_mask, padding_mask