code: def padded_tensor(
      items: List[Union[List[int], torch.LongTensor]],
      pad_idx: int = 0,
      left_padded: bool = False,
      max_len: Optional[int] = None,
      fp16friendly: bool = False,
  ) -> Tuple[torch.LongTensor, List[int]]:
      """
      Create a padded matrix from an uneven list of lists.
  
      Returns (padded, lengths), where padded is the padded matrix, and lengths
      is a list containing the lengths of each row.
  
      Matrix is right-padded (filled to the right) by default, but can be
      left padded if the flag is set to True.
  
      Matrix can also be placed on cuda automatically.
  
      :param list[iter[int]] items: List of items
      :param bool sort: If True, orders by the length
      :param int pad_idx: the value to use for padding
      :param bool left_padded:
      :param int max_len: if None, the max length is the maximum item length
      :param bool fp16friendly: if True, pads the time dimension to be a multiple of 4.
  
      :returns: (padded, lengths) tuple
      :rtype: (Tensor[int64], list[int])
      """
  
      # number of items
      n = len(items)
      # length of each item
      lens: List[int] = [len(item) for item in items]  # type: ignore
      # max in time dimension
      t = max(lens) if max_len is None else max_len
  
      # if input tensors are empty, we should expand to nulls
      t = max(t, 1)
  
      if fp16friendly and (t % FP16_PAD_SIZE != 0):
          # pad to be fp16 friendly
          t += FP16_PAD_SIZE - (t % FP16_PAD_SIZE)
  
      if isinstance(items[0], torch.Tensor):
          # keep type of input tensors, they may already be cuda ones
          output = items[0].new(n, t)  # type: ignore
      else:
          output = torch.LongTensor(n, t)  # type: ignore
      output.fill_(pad_idx)
  
      for i, (item, length) in enumerate(zip(items, lens)):
          if length == 0:
              # skip empty items
              continue
          if not isinstance(item, torch.Tensor):
              # put non-tensors into a tensor
              item = torch.LongTensor(item)  # type: ignore
          if left_padded:
              # place at end
              output[i, t - length :] = item
          else:
              # place at beginning
              output[i, :length] = item
  
      return output, lens
  
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 3 for calling a global function with example usage: padded_tensor(items, pad_idx, left_padded, max_len, fp16friendly) and returns: output,  lens