code: def stack_tensors(
      tensors: List[torch.Tensor],
      max_lens: List[int],
      pad_value: float = 0.0,
  ) -> torch.Tensor:
      """
      Create batch by stacking input tensor list along the time axes.
  
      Args:
          tensors: List of tensors to pad and stack
          max_lens: List of lengths to pad each axis to, starting with the last axis
          pad_value: Value for padding
  
      Returns:
          Padded and stacked tensor.
      """
      padded_tensors = []
      for tensor in tensors:
          padding = []
          for i, max_len in enumerate(max_lens, 1):
              padding += [0, max_len - tensor.shape[-i]]
  
          padded_tensor = torch.nn.functional.pad(
              tensor, pad=padding, value=pad_value
          )
          padded_tensors.append(padded_tensor)
  
      stacked_tensor = torch.stack(padded_tensors)
      return stacked_tensor
  
imports: import functools
  import os
  import random
  import traceback
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple
  import librosa
  import numpy
  import torch
  from einops import rearrange
  from scipy import ndimage
  from torch.special import gammaln
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
label_desc: Use image node_id 5 for calling a global function with example usage: stack_tensors(tensors, max_lens, pad_value) and returns: stacked_tensor