code: def __beaver_protocol(ctx, op, x, y, *args, **kwargs):
      """Performs Beaver protocol for additively secret-shared tensors x and y
  
      1. Obtain uniformly random sharings [a],[b] and [c] = [a * b]
      2. Additively hide [x] and [y] with appropriately sized [a] and [b]
      3. Open ([epsilon] = [x] - [a]) and ([delta] = [y] - [b])
      4. Return [z] = [c] + (epsilon * [b]) + ([a] * delta) + (epsilon * delta)
      """
      assert op in {
          "mul",
          "matmul",
          "conv1d",
          "conv2d",
          "conv_transpose1d",
          "conv_transpose2d",
      }
      if x.device != y.device:
          raise ValueError(
              f"x lives on device {x.device} but y on device {y.device}"
          )
  
      provider = mpc.get_default_provider()
      a, b, c = provider.generate_additive_triple(
          ctx, x.size(), y.size(), op, device=x.device, *args, **kwargs
      )
  
      from .arithmetic import ArithmeticSharedTensor
  
      if cfg.safety.mpc.active_security:
          """
          Reference: "Multiparty Computation from Somewhat Homomorphic Encryption"
          Link: https://eprint.iacr.org/2011/535.pdf
          """
          f, g, h = provider.generate_additive_triple(
              ctx,
              x.size(),
              y.size(),
              op,
              device=x.device,
              *args,
              **kwargs,
          )
  
          t = ArithmeticSharedTensor.PRSS(
              ctx, a.size(), device=x.device
          )
          t_plain_text = t.get_plain_text()
  
          rho = (t_plain_text * a - f).get_plain_text()
          sigma = (b - g).get_plain_text()
          triples_check = (
              t_plain_text * c - h - sigma * f - rho * g - rho * sigma
          )
          triples_check = triples_check.get_plain_text()
  
          if torch.any(triples_check != 0):
              raise ValueError("Beaver Triples verification failed!")
  
      # Vectorized reveal to reduce rounds of communication
      with IgnoreEncodings([a, b, x, y]):
          epsilon, delta = ArithmeticSharedTensor.reveal_batch(
              [x - a, y - b]
          )
  
      # z = c + (a * delta) + (epsilon * b) + epsilon * delta
      c._tensor += getattr(torch, op)(
          epsilon, b._tensor, *args, **kwargs
      )
      c._tensor += getattr(torch, op)(a._tensor, delta, *args, **kwargs)
      c += getattr(torch, op)(epsilon, delta, *args, **kwargs)
  
      return c
  
imports: import torch
  import fate.arch.protocol.mpc.communicator
  from fate.arch.protocol import mpc
  from fate.arch.protocol.mpc.common.util import count_wraps
  from fate.arch.protocol.mpc.config import cfg
label_desc: Use image node_id 1 for calling a global function with example usage: __beaver_protocol(ctx, op, x, y) and returns: c