code: def objective_rosenbrok(params):
      lr = params["lr"]
      optimizer_class = params["optimizer_class"]
      minimum = (1.0, 1.0)
      initial_state = (-2.0, 2.0)
      optimizer_config = dict(lr=lr)
      num_iter = 100
      steps = execute_steps(
          rosenbrock,
          initial_state,
          optimizer_class,
          optimizer_config,
          num_iter,
      )
      return (steps[0][-1] - minimum[0]) ** 2 + (
          steps[1][-1] - minimum[1]
      ) ** 2
  
file_path: pytorch-optimizer/examples/viz_optimizers.py
imports: import math
  import matplotlib.pyplot
  import numpy
  import torch
  from hyperopt import fmin, hp, tpe
  import torch_optimizer
label_desc: Use image node_id 5 for calling a global function with example usage: objective_rosenbrok(params) and returns: unknown