code: def stack(tensors, dim=0):
      """
      Stacks the specified CrypTen `tensors` along dimension `dim`. In contrast to
      `crypten.cat`, this adds a dimension to the result tensor.
      """
      assert isinstance(tensors, list), "input to stack must be a list"
      assert all(
          isinstance(t, CrypTensor) for t in tensors
      ), "inputs must be CrypTensors"
      tensor_types = [get_cryptensor_type(t) for t in tensors]
      assert all(
          ttype == tensor_types[0] for ttype in tensor_types
      ), "cannot stack CrypTensors with different underlying types"
      if len(tensors) == 1:
          return tensors[0].unsqueeze(dim)
      return type(tensors[0]).stack(tensors, dim=dim)
  
imports: import builtins
  import copy
  import logging
  import os
  import typing
  import torch
  from .None import nn
  from .None import primitives, provider
  from .communicator import Communicator
  from .config import cfg
  from .cryptensor import CrypTensor
  from .mpc import MPCTensor
  from .ptype import ptype
label_desc: Use image node_id 12 for calling a global function with example usage: stack(tensors, dim) and returns: type, tensors