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).
class_docstr_tok: ["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: Call `torch.Tensor.cuda` on the underlying _tensor
docstr_tok: ["Call","`","torch.Tensor.cuda","`","on","the","underlying","_tensor"]
code: def cuda(self, *args, **kwargs):
      """Call `torch.Tensor.cuda` on the underlying _tensor"""
      self._tensor = CUDALongTensor(self._tensor.cuda(*args, **kwargs))
      return self
  
imports: import crypten.communicator
  import torch
  from crypten.common.functions import regular
  from crypten.common.rng import generate_kbit_random_tensor
  from crypten.common.tensor_types import is_tensor
  from crypten.common.util import torch_cat, torch_stack
  from crypten.cuda import CUDALongTensor
  from crypten.encoder import FixedPointEncoder
  from .None import beaver, circuit
label_desc: Use image node_id 9 for calling the BinarySharedTensor obj's underlying member method code with example usage: obj.cuda() and returns: self