code: def forward_padding_mask(
      self,
      features: torch.Tensor,
      padding_mask: torch.Tensor,
  ) -> torch.Tensor:
      extra = padding_mask.size(1) % features.size(1)
      if extra > 0:
          padding_mask = padding_mask[:, :-extra]
      padding_mask = padding_mask.view(
          padding_mask.size(0), features.size(1), -1
      )
      padding_mask = padding_mask.all(-1)
      return padding_mask
  
file_path: lavis/lavis/models/beats/Tokenizers.py
imports: import torch
  import torch.nn
  from torch.nn import LayerNorm
  import torchaudio.compliance.kaldi
  from lavis.models.beats.backbone import TransformerEncoder
  from lavis.models.beats.quantizer import NormEMAVectorQuantizer
  import logging
  from typing import Optional
label_desc: Use image node_id 2 for calling the Tokenizers obj's underlying member method code with example usage: obj.forward_padding_mask(features, padding_mask) and returns: padding_mask