code: def loss_step(
      X, y, x_, tensor_name, loss="logistic_regression", **kwargs
  ):
      """Calculate gradient via pytorch autograd."""
  
      if os.getenv("BLUEFOG_TIMELINE"):
          with bf.timeline_context(
              tensor_name=tensor_name,
              activity_name="gradient computation",
          ):
              return _loss_step(X, y, x_, loss=loss, **kwargs)
  
      else:
          return _loss_step(X, y, x_, loss=loss, **kwargs)
  
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 4 for calling a global function with example usage: loss_step(X, y, x_, tensor_name, loss) and returns: _loss_step, _loss_step