code: def padded_3d(
      tensors: List[torch.LongTensor],
      pad_idx: int = 0,
      dtype: Optional[torch.dtype] = torch.long,
      fp16friendly: bool = False,
  ):
      """
      Make 3D padded tensor for list of lists of 1D tensors or lists.
  
      Will keep items on the same device as originally.
  
      :param tensors:
          list of lists of 1D tensors (or lists)
      :param pad_idx:
          padding to fill tensor with
      :param bool fp16friendly:
          if True, pads the final dimension to be a multiple of 8.
  
      :returns:
          3D tensor with the maximum dimensions of the inputs
      """
      a = len(tensors)
      b = max(len(row) for row in tensors)  # type: ignore
      c = max(len(item) for row in tensors for item in row)  # type: ignore
  
      # pad empty tensors
      if fp16friendly and c % FP16_PAD_SIZE != 0:
          c += FP16_PAD_SIZE - (c % FP16_PAD_SIZE)
      c = max(c, 1)
  
      dev = tensors[0][0].device
      output = torch.full((a, b, c), pad_idx, dtype=dtype, device=dev)
  
      for i, row in enumerate(tensors):
          item: Sized
          for j, item in enumerate(row):  # type: ignore
              if len(item) == 0:
                  continue
              if not isinstance(item, torch.Tensor):
                  item = torch.as_tensor(item, dtype=dtype)
              output[i, j, : len(item)] = item
  
      return output
  
imports: import os
  from typing import Union, Optional, Tuple, Any, List, Sized, TypeVar
  import itertools
  from collections import namedtuple
  import parlai.utils.logging
  import parlai.utils.io
  import torch.optim
label_desc: Use image node_id 4 for calling a global function with example usage: padded_3d(tensors, pad_idx, dtype, fp16friendly) and returns: output