code: def batch_from_ragged(
      text: torch.Tensor,
      pitch: torch.Tensor,
      pace: torch.Tensor,
      batch_lengths: torch.Tensor,
      padding_idx: int = -1,
      volume: Optional[torch.Tensor] = None,
  ) -> Tuple[
      torch.Tensor,
      torch.Tensor,
      torch.Tensor,
      torch.Tensor,
      torch.Tensor,
  ]:
      batch_lengths = batch_lengths.to(dtype=torch.int64)
      max_len = torch.max(batch_lengths[1:] - batch_lengths[:-1])
  
      index = 1
      num_batches = batch_lengths.shape[0] - 1
      texts = (
          torch.zeros(
              num_batches,
              max_len,
              dtype=torch.int64,
              device=text.device,
          )
          + padding_idx
      )
      pitches = torch.ones(
          num_batches, max_len, dtype=torch.float32, device=text.device
      )
      paces = (
          torch.zeros(
              num_batches,
              max_len,
              dtype=torch.float32,
              device=text.device,
          )
          + 1.0
      )
      volumes = (
          torch.zeros(
              num_batches,
              max_len,
              dtype=torch.float32,
              device=text.device,
          )
          + 1.0
      )
      lens = torch.zeros(
          num_batches, dtype=torch.int64, device=text.device
      )
      last_index = index - 1
      while index < batch_lengths.shape[0]:
          seq_start = batch_lengths[last_index]
          seq_end = batch_lengths[index]
          cur_seq_len = seq_end - seq_start
          lens[last_index] = cur_seq_len
          texts[last_index, :cur_seq_len] = text[seq_start:seq_end]
          pitches[last_index, :cur_seq_len] = pitch[seq_start:seq_end]
          paces[last_index, :cur_seq_len] = pace[seq_start:seq_end]
          if volume is not None:
              volumes[last_index, :cur_seq_len] = volume[
                  seq_start:seq_end
              ]
          last_index = index
          index += 1
  
      return texts, pitches, paces, volumes, lens
  
imports: from enum import Enum
  from typing import Optional, Tuple
  import librosa
  import matplotlib.pylab
  import numpy
  import torch
  from einops import rearrange
  from numba import jit, prange
  from nemo.collections.tts.torch.tts_data_types import DATA_STR2DATA_CLASS, MAIN_DATA_TYPES, WithLens
  from nemo.utils import logging
  from nemo.utils.decorators import deprecated
label_desc: Use image node_id 34 for calling a global function with example usage: batch_from_ragged(text, pitch, pace, batch_lengths, padding_idx, volume) and returns: texts,  pitches,  paces,  volumes,  lens