code: def _loss_step(X, y, x_, loss="logistic_regression", **kwargs):
      """Calculate gradient via pytorch autograd."""
  
      if loss == "logistic_regression":
          rho = kwargs.get("rho", 1e-1)
          loss_ = torch.mean(
              torch.log(1 + torch.exp(-y * X.mm(x_)))
          ) + 0.5 * rho * torch.norm(x_, p=2)
      elif loss == "linear_regression":
          loss_ = 0.5 * torch.mean(torch.norm(X.mm(x_) - y, p=2))
  
      else:
          raise NotImplementedError(
              "Task not supported. This example only supports"
              + " linear_regression and logistic_regression"
          )
  
      loss_.backward()
  
      return loss_
  
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 3 for calling a global function with example usage: _loss_step(X, y, x_, loss) and returns: loss_