code: def diffusion(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"
          )
  
      topology = bf.load_topology()
      self_weight, neighbor_weights = topology_util.GetRecvWeights(
          topology, bf.rank()
      )
  
      w = torch.zeros(n, 1, dtype=torch.double, requires_grad=True)
      phi = w.clone()
      mse = []
  
      for i in range(maxite):
          # calculate loccal gradient via pytorch autograd
          loss_step(
              X,
              y,
              w,
              tensor_name="neighbor.allreduce.local_variable",
              loss=loss,
              rho=rho,
          )
  
          # diffusion
          with torch.no_grad():
              phi = w - alpha * w.grad.data
              w.data = bf.neighbor_allreduce(
                  phi,
                  self_weight=self_weight,
                  src_weights=neighbor_weights,
                  name="local variable",
              )
              w.grad.data.zero_()
  
              # record convergence
              if bf.rank() == 0:
                  mse.append(torch.norm(w.data - w_opt.data, p=2))
  
      return w, 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 6 for calling a global function with example usage: diffusion(X, y, w_opt, loss, maxite, alpha) and returns: w,  mse