code: def wraps(x):
      """Privately computes the number of wraparounds for a set a shares
  
      To do so, we note that:
          [theta_x] = theta_z + [beta_xr] - [theta_r] - [eta_xr]
  
      Where [theta_i] is the wraps for a variable i
            [beta_ij] is the differential wraps for variables i and j
            [eta_ij]  is the plaintext wraps for variables i and j
  
      Note: Since [eta_xr] = 0 with probability 1 - |x| / Q for modulus Q, we
      can make the assumption that [eta_xr] = 0 with high probability.
      """
      provider = mpc.get_default_provider()
      r, theta_r = provider.wrap_rng(x.size(), device=x.device)
      beta_xr = theta_r.clone()
      beta_xr._tensor = count_wraps([x._tensor, r._tensor])
  
      with IgnoreEncodings([x, r]):
          z = x + r
      theta_z = comm.get().gather(z._tensor, 0)
      theta_x = beta_xr - theta_r
  
      # TODO: Incorporate eta_xr
      if x.rank == 0:
          theta_z = count_wraps(theta_z)
          theta_x._tensor += theta_z
      return theta_x
  
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 9 for calling a global function with example usage: wraps(x) and returns: theta_x