class_docstr: VGG2L module for custom encoder.
  
  Args:
      idim: Input dimension.
      odim: Output dimension.
      pos_enc: Positional encoding class.
docstr: Create a subsampled mask of feature sequences.
  
  Args:
      feats_mask: Mask of feature sequences. (B, 1, F)
  
  Returns:
      vgg_mask: Mask of VGG2L output sequences. (B, 1, sub(F))
docstr_tok: ["Create","a","subsampled","mask","of","feature","sequences",".","Args",":","feats_mask",":","Mask","of","feature","sequences",".","(","B",",","1",",","F",")","Returns",":","vgg_mask",":","Mask","of","VGG2L","output","sequences",".","(","B",",","1",",","sub","(","F",")",")"]
code: def create_new_mask(self, feats_mask: torch.Tensor) -> torch.Tensor:
      """Create a subsampled mask of feature sequences.
  
      Args:
          feats_mask: Mask of feature sequences. (B, 1, F)
  
      Returns:
          vgg_mask: Mask of VGG2L output sequences. (B, 1, sub(F))
  
      """
      vgg1_t_len = feats_mask.size(2) - (feats_mask.size(2) % 3)
      vgg_mask = feats_mask[:, :, :vgg1_t_len][:, :, ::3]
  
      vgg2_t_len = vgg_mask.size(2) - (vgg_mask.size(2) % 2)
      vgg_mask = vgg_mask[:, :, :vgg2_t_len][:, :, ::2]
  
      return vgg_mask
  
imports: from typing import Tuple, Union
  import torch
label_desc: Use image node_id 3 for calling the VGG2L obj's underlying member method code with example usage: obj.create_new_mask(feats_mask) and returns: vgg_mask