code: def model_fitness(x):
      learning_rate, num_dense_layers, num_dense_nodes, activation = x
  
      fitness = (
          ((exp(learning_rate) - 1.0) * 1000) ** 2
          + (num_dense_layers) ** 2
          + (num_dense_nodes / 100) ** 2
      )
  
      fitness *= 1.0 + 0.1 * np.random.rand()
  
      if activation == "sigmoid":
          fitness += 10
  
      return fitness
  
file_path: scikit-optimize/examples/plots/partial-dependence-plot-2D.py
imports: import numpy
  from math import exp
  from skopt import gp_minimize
  from skopt.space import Real, Categorical, Integer
  from skopt.plots import plot_histogram, plot_objective_2D, plot_objective
  from skopt.utils import point_asdict
  import matplotlib.pyplot
label_desc: Use image node_id 1 for calling a global function with example usage: model_fitness(x) and returns: fitness