class_docstr: Encrypted tensor object that uses binary sharing to perform computations.
  
  Binary shares are computed by splitting each value of the input tensor
  into n separate random values that xor together to the input tensor value,
  where n is the number of parties present in the protocol (world_size).
docstr: Stacks a list of tensors along a given dimension
docstr_tok: ["Stacks","a","list","of","tensors","along","a","given","dimension"]
code: def stack(seq, *args, **kwargs):
      """Stacks a list of tensors along a given dimension"""
      assert isinstance(seq, list), "Stack input must be a list"
      assert isinstance(
          seq[0], BinarySharedTensor
      ), "Sequence must contain BinarySharedTensors"
      result = seq[0].shallow_copy()
      result.share = torch_stack(
          [BinarySharedTensor.share for BinarySharedTensor in seq],
          *args,
          **kwargs,
      )
      return result
  
imports: import torch
  import fate.arch.protocol.mpc.communicator
  from fate.arch.protocol.mpc.common.rng import generate_kbit_random_tensor
  from fate.arch.protocol.mpc.common.tensor_types import is_tensor
  from fate.arch.protocol.mpc.common.util import torch_cat, torch_stack
  from fate.arch.protocol.mpc.cuda import CUDALongTensor
  from fate.arch.protocol.mpc.encoder import FixedPointEncoder
  from fate.arch.protocol.mpc.functions import regular
  from .None import beaver, circuit
label_desc: Use image node_id 39 for calling the BinarySharedTensor obj's underlying member method code with example usage: obj.stack(seq) and returns: result