code: def text_mel_collate_fn(
      batch: Tuple[Tensor, Tensor], n_frames_per_step: int = 1
  ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
      """The collate function padding and adjusting the data based on `n_frames_per_step`.
      Modified from https://github.com/NVIDIA/DeepLearningExamples
  
      Args:
          batch (tuple of two tensors): the first tensor is the mel spectrogram with shape
              (n_batch, n_mels, n_frames), the second tensor is the text with shape (n_batch, ).
          n_frames_per_step (int, optional): The number of frames to advance every step.
  
      Returns:
          text_padded (Tensor): The input text to Tacotron2 with shape (n_batch, max of ``text_lengths``).
          text_lengths (Tensor): The length of each text with shape (n_batch).
          mel_specgram_padded (Tensor): The target mel spectrogram
              with shape (n_batch, n_mels, max of ``mel_specgram_lengths``)
          mel_specgram_lengths (Tensor): The length of each mel spectrogram with shape (n_batch).
          gate_padded (Tensor): The ground truth gate output
              with shape (n_batch, max of ``mel_specgram_lengths``)
      """
      text_lengths, ids_sorted_decreasing = torch.sort(
          torch.LongTensor([len(x[0]) for x in batch]),
          dim=0,
          descending=True,
      )
      max_input_len = text_lengths[0]
  
      text_padded = torch.zeros(
          (len(batch), max_input_len), dtype=torch.int64
      )
      for i in range(len(ids_sorted_decreasing)):
          text = batch[ids_sorted_decreasing[i]][0]
          text_padded[i, : text.size(0)] = text
  
      # Right zero-pad mel-spec
      num_mels = batch[0][1].size(0)
      max_target_len = max([x[1].size(1) for x in batch])
      if max_target_len % n_frames_per_step != 0:
          max_target_len += (
              n_frames_per_step - max_target_len % n_frames_per_step
          )
          assert max_target_len % n_frames_per_step == 0
  
      # include mel padded and gate padded
      mel_specgram_padded = torch.zeros(
          (len(batch), num_mels, max_target_len), dtype=torch.float32
      )
      gate_padded = torch.zeros(
          (len(batch), max_target_len), dtype=torch.float32
      )
      mel_specgram_lengths = torch.LongTensor(len(batch))
      for i in range(len(ids_sorted_decreasing)):
          mel = batch[ids_sorted_decreasing[i]][1]
          mel_specgram_padded[i, :, : mel.size(1)] = mel
          mel_specgram_lengths[i] = mel.size(1)
          gate_padded[i, mel.size(1) - 1 :] = 1
  
      return (
          text_padded,
          text_lengths,
          mel_specgram_padded,
          mel_specgram_lengths,
          gate_padded,
      )
  
imports: from typing import Callable, List, Tuple
  import torch
  from torch import Tensor
  from torch.utils.data.dataset import random_split
  from torchaudio.datasets import LJSPEECH
label_desc: Use image node_id 2 for calling a global function with example usage: text_mel_collate_fn(batch, n_frames_per_step) and returns: text_padded,  text_lengths,  mel_specgram_padded,  mel_specgram_lengths,  gate_padded