code: def padded_stack(
      tensors: List[torch.Tensor],
      side: str = "right",
      mode: str = "constant",
      value: Union[int, float] = 0,
  ) -> torch.Tensor:
      """
      Stack tensors along first dimension and pad them along last dimension to ensure their size is equal.
  
      Args:
          tensors (List[torch.Tensor]): list of tensors to stack
          side (str): side on which to pad - "left" or "right". Defaults to "right".
          mode (str): 'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'
          value (Union[int, float]): value to use for constant padding
  
      Returns:
          torch.Tensor: stacked tensor
      """
      full_size = max([x.size(-1) for x in tensors])
  
      def make_padding(pad):
          if side == "left":
              return (pad, 0)
          elif side == "right":
              return (0, pad)
          else:
              raise ValueError(f"side for padding '{side}' is unknown")
  
      out = torch.stack(
          [
              F.pad(
                  x,
                  make_padding(full_size - x.size(-1)),
                  mode=mode,
                  value=value,
              )
              if full_size - x.size(-1) > 0
              else x
              for x in tensors
          ],
          dim=0,
      )
      return out
  
imports: from collections import namedtuple
  from contextlib import redirect_stdout
  import inspect
  import os
  from typing import Any, Callable, Dict, List, Tuple, Union
  import lightning.pytorch
  import torch
  from torch import nn
  from torch.fft import irfft, rfft
  import torch.nn.functional
  from torch.nn.utils import rnn
label_desc: Use image node_id 10 for calling a global function with example usage: padded_stack(tensors, side, mode, value) and returns: out, pad,  int, int,  pad