code: def collate(
      tensors: tp.List[torch.Tensor], dim: int = 0
  ) -> tp.Tuple[torch.Tensor, torch.Tensor]:
      """Get a list of tensors and collate them to a single tensor. according to the following logic:
      - `dim` specifies the time dimension which will be stacked and padded.
      - The output will contain 1 new dimension (dimension index 0) which will be the size of
      of the original list.
  
      Args:
          tensors (tp.List[torch.Tensor]): List of tensors to collate.
          dim (int): Dimension which will be stacked and padded.
      Returns:
          tp.Tuple[torch.Tensor, torch.Tensor]:
              torch.Tensor: Stacked and padded tensor. The output will contain 1 new dimension
                  (dimension index 0) which will be the size of the original list.
              torch.Tensor: Tensor containing length of original tensor sizes (without padding).
      """
      tensors = [x.transpose(0, dim) for x in tensors]
      lens = torch.LongTensor([len(x) for x in tensors])
      padded_tensors = pad_sequence(tensors)
      padded_tensors = padded_tensors.transpose(0, 1)
      padded_tensors = padded_tensors.transpose(1, dim + 1)
      return padded_tensors, lens
  
imports: from concurrent.futures import ProcessPoolExecutor
  from contextlib import contextmanager
  from functools import wraps, lru_cache
  import hashlib
  import json
  import logging
  from pathlib import Path
  import typing
  import flashy
  import flashy.distrib
  import omegaconf
  import torch
  from torch.nn.utils.rnn import pad_sequence
label_desc: Use image node_id 13 for calling a global function with example usage: collate(tensors, dim) and returns: padded_tensors,  lens