code: def regulate_len(
      durations,
      enc_out,
      pace=1.0,
      mel_max_len=None,
      group_size=1,
      dur_lens: torch.tensor = None,
  ):
      """A function that takes predicted durations per encoded token, and repeats enc_out according to the duration.
      NOTE: durations.shape[1] == enc_out.shape[1]
  
      Args:
          durations (torch.tensor): A tensor of shape (batch x enc_length) that represents how many times to repeat each
              token in enc_out.
          enc_out (torch.tensor): A tensor of shape (batch x enc_length x enc_hidden) that represents the encoded tokens.
          pace (float): The pace of speaker. Higher values result in faster speaking pace. Defaults to 1.0.        max_mel_len (int): The maximum length above which the output will be removed. If sum(durations, dim=1) >
              max_mel_len, the values after max_mel_len will be removed. Defaults to None, which has no max length.
          group_size (int): replicate the last element specified by durations[i, in_lens[i] - 1] until the
              full length of the sequence is the next nearest multiple of group_size
          in_lens (torch.tensor): input sequence length specifying valid values in the durations input tensor (only needed if group_size >1)
      """
  
      dtype = enc_out.dtype
      reps = durations.float() / pace
      reps = (reps + 0.5).floor().long()
      dec_lens = reps.sum(dim=1)
      if group_size > 1:
          to_pad = (
              group_size
              * (
                  torch.div(
                      dec_lens + 1, group_size, rounding_mode="floor"
                  )
              )
              - dec_lens
          )
          reps.index_put_(
              indices=[
                  torch.arange(dur_lens.shape[0], dtype=torch.long),
                  dur_lens - 1,
              ],
              values=to_pad,
              accumulate=True,
          )
          dec_lens = reps.sum(dim=1)
  
      max_len = dec_lens.max()
      reps_cumsum = torch.cumsum(
          torch.nn.functional.pad(reps, (1, 0, 0, 0), value=0.0), dim=1
      )[:, None, :]
      reps_cumsum = reps_cumsum.to(dtype=dtype, device=enc_out.device)
  
      range_ = torch.arange(max_len).to(enc_out.device)[None, :, None]
      mult = (reps_cumsum[:, :, :-1] <= range_) & (
          reps_cumsum[:, :, 1:] > range_
      )
      mult = mult.to(dtype)
      enc_rep = torch.matmul(mult, enc_out)
  
      if mel_max_len is not None:
          enc_rep = enc_rep[:, :mel_max_len]
          dec_lens = torch.clamp_max(dec_lens, mel_max_len)
  
      return enc_rep, dec_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 24 for calling a global function with example usage: regulate_len(durations, enc_out, pace, mel_max_len, group_size, dur_lens) and returns: enc_rep,  dec_lens