code: def distributed_grad_descent(
      X, y, loss, maxite=5000, 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"
          )
  
      w_opt = torch.zeros(n, 1, dtype=torch.double, requires_grad=True)
  
      for _ in range(maxite):
          # calculate gradient via pytorch autograd
          loss_step(
              X,
              y,
              w_opt,
              tensor_name="allreduce.gradient",
              loss=loss,
              rho=rho,
          )
          # global gradient
          grad = bf.allreduce(w_opt.grad.data, name="gradient")
  
          # distributed gradient descent
          w_opt.data = w_opt.data - alpha * grad
          w_opt.grad.data.zero_()
  
      loss_step(
          X,
          y,
          w_opt,
          tensor_name="allreduce.gradient",
          loss=loss,
          rho=rho,
      )
      grad = bf.allreduce(
          w_opt.grad.data, name="gradient"
      )  # global gradient
  
      # evaluate the convergence of distributed logistic regression
      # the norm of global gradient is expected to 0 (optimality condition)
      global_grad_norm = torch.norm(grad, p=2)
      print(
          "[DG] Rank {}: global gradient norm: {}".format(
              bf.rank(), global_grad_norm
          )
      )
  
      # the norm of local gradient is expected not be be close to 0
      # this is because each rank converges to global solution, not local solution
      local_grad_norm = torch.norm(w_opt.grad.data, p=2)
      print(
          "[DG] Rank {}: local gradient norm: {}".format(
              bf.rank(), local_grad_norm
          )
      )
  
      return w_opt
  
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 5 for calling a global function with example usage: distributed_grad_descent(X, y, loss, maxite, alpha) and returns: w_opt