code: def push_diging(X, y, w_opt, loss, maxite=2000, alpha=1e-1, **kwargs):
      if loss == "logistic_regression":
          rho = kwargs.get("rho", 1e-1)
      elif loss == "linear_regression":
          rho = 0
      else:
          raise NotImplementedError(
              "Task not supported. This example only supports"
              + " linear_regression and logistic_regression"
          )
  
      outdegree = len(bf.out_neighbor_ranks())
      indegree = len(bf.in_neighbor_ranks())
  
      # We let w = col{u, y, v}, i.e., u, y, v = w[:n], w[n:2*n], w[2n]
      # Insteady of three directed_neighbor_allreduce operations for u, y,
      # and v respectively, we exploit one directed_neighbor_allreduce for
      # the combo vector w. This guarantees u, y, and v to be transmitted
      # simultanesly and avoids the mismatch between them. Experiments
      # show directed_neighbor_allreduce(w) is crutial for convergence of
      # push_diging.
      w = torch.zeros(2 * n + 1, 1).to(torch.double)
      x = torch.zeros(n, 1, dtype=torch.double, requires_grad=True)
      loss_step(X, y, x, tensor_name="w_buff", loss=loss, rho=rho)
  
      grad = x.grad.data.clone()
      w[n : 2 * n] = grad
      x.grad.data.zero_()
  
      w[-1] = 1.0
      grad_prev = w[n : 2 * n].clone()
  
      bf.win_create(w, name="w_buff", zero_init=True)
  
      mse = []
      for _ in range(maxite):
          bf.barrier()
  
          w[:n] = w[:n] - alpha * w[n : 2 * n]
          bf.win_accumulate(
              w,
              name="w_buff",
              dst_weights={
                  rank: 1.0 / (outdegree * 2)
                  for rank in bf.out_neighbor_ranks()
              },
              require_mutex=True,
          )
          w.div_(2)
          bf.barrier()
  
          w = bf.win_update_then_collect(name="w_buff")
  
          x.data = w[:n] / w[-1]
          loss_step(X, y, x, tensor_name="w_buff", loss=loss, rho=rho)
          grad = x.grad.data.clone()
          x.grad.data.zero_()
  
          w[n : 2 * n] += grad - grad_prev
          grad_prev = grad
          if bf.rank() == 0:
              mse.append(torch.norm(x.data - w_opt, p=2))
  
      bf.barrier()
      w = bf.win_update_then_collect(name="w_buff")
      x.data = w[:n] / w[-1]
  
      return x, mse
  
file_path: bluefog/examples/pytorch_optimization.py
imports: import os
  import torch
  import matplotlib
  import matplotlib.pyplot
  import argparse
  import bluefog.torch
  from bluefog.common import topology_util
  import networkx
label_desc: Use image node_id 9 for calling a global function with example usage: push_diging(X, y, w_opt, loss, maxite, alpha) and returns: x,  mse