class_docstr: VGG + Transformer encoder
class_docstr_tok: ["VGG","+","Transformer","encoder"]
docstr: embedding is a (T, B, D) tensor
  padding_mask is a (B, T) tensor or None
  attn_mask is a (T, T) tensor or None
docstr_tok: ["embedding","is","a","(","T",",","B",",","D",")","tensor","padding_mask","is","a","(","B",",","T",")","tensor","or","None","attn_mask","is","a","(","T",",","T",")","tensor","or","None"]
code: def slice(self, embedding, padding_mask, attn_mask, sampling_factor):
      """
      embedding is a (T, B, D) tensor
      padding_mask is a (B, T) tensor or None
      attn_mask is a (T, T) tensor or None
      """
      embedding = embedding[::sampling_factor, :, :]
      if padding_mask is not None:
          padding_mask = padding_mask[:, ::sampling_factor]
      if attn_mask is not None:
          attn_mask = attn_mask[::sampling_factor, ::sampling_factor]
  
      return embedding, padding_mask, attn_mask
  
imports: import argparse
  import math
  from collections.abc import Iterable
  import torch
  import torch.nn
  from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import LinearizedConvolution, TransformerDecoderLayer, TransformerEncoderLayer, VGGBlock
label_desc: Use image node_id 7 for calling the VGGTransformerEncoder obj's underlying member method code with example usage: obj.slice(embedding, padding_mask, attn_mask, sampling_factor) and returns: embedding,  padding_mask,  attn_mask