code: def make_transformer_src_tgt_masks(
      src,
      tgt=None,
      wav_len=None,
      pad_idx=0,
      causal: bool = False,
      dynchunktrain_config: Optional[DynChunkTrainConfig] = None,
  ):
      """This function generates masks for training the transformer model,
      opiniated for an ASR context with encoding masks and, optionally, decoding
      masks (if specifying `tgt`).
  
      Arguments
      ---------
      src : tensor
          The sequence to the encoder (required).
      tgt : tensor
          The sequence to the decoder.
      pad_idx : int
          The index for <pad> token (default=0).
      causal: bool
          Whether strict causality shall be used. See `make_asr_src_mask`
      dynchunktrain_config: DynChunkTrainConfig, optional
          Dynamic Chunk Training configuration. See `make_asr_src_mask`
      """
      src_key_padding_mask = None
  
      # mask out audio beyond the length of audio for each batch
      if wav_len is not None:
          abs_len = torch.round(wav_len * src.shape[1])
          src_key_padding_mask = ~length_to_mask(abs_len).bool()
  
      # mask out the source
      src_mask = make_transformer_src_mask(
          src, causal=causal, dynchunktrain_config=dynchunktrain_config
      )
  
      # If no decoder in the transformer...
      if tgt is not None:
          tgt_key_padding_mask = get_key_padding_mask(
              tgt, pad_idx=pad_idx
          )
          tgt_mask = get_lookahead_mask(tgt)
      else:
          tgt_key_padding_mask = None
          tgt_mask = None
  
      return (
          src_key_padding_mask,
          tgt_key_padding_mask,
          src_mask,
          tgt_mask,
      )
  
imports: from dataclasses import dataclass
  import torch
  from torch import nn
  from typing import Any, Optional
  from speechbrain.nnet.linear import Linear
  from speechbrain.nnet.containers import ModuleList
  from speechbrain.lobes.models.transformer.Transformer import TransformerInterface, get_lookahead_mask, get_key_padding_mask, NormalizedEmbedding
  from speechbrain.nnet.activations import Swish
  from speechbrain.dataio.dataio import length_to_mask
  from speechbrain.utils.dynamic_chunk_training import DynChunkTrainConfig
label_desc: Use image node_id 2 for calling a global function with example usage: make_transformer_src_tgt_masks(src, tgt, wav_len, pad_idx, causal, dynchunktrain_config) and returns: src_key_padding_mask,  tgt_key_padding_mask,  src_mask,  tgt_mask