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: Decrypts the tensor.
docstr_tok: ["Decrypts","the","tensor","."]
code: def get_plain_text(self, dst=None):
      """Decrypts the tensor."""
      # Edge case where share becomes 0 sized (e.g. result of split)
      if self.nelement() < 1:
          return torch.empty(self.share.size())
      return self.encoder.decode(self.reveal(dst=dst))
  
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 45 for calling the BinarySharedTensor obj's underlying member method code with example usage: obj.get_plain_text(dst) and returns: self, torch