code: def evaluate(
      weight_1, weight_2, weight_3, weight_4, weight_5, alpha1, alpha2
  ):
      weights = [weight_1, weight_2, weight_3, weight_4, weight_5]
  
      portfolio_return = sum(
          weights[i] * expected_returns_simulated[i] for i in range(5)
      )
  
      portfolio_volatility = sum(
          weights[i] * weights[j] * covariance_matrix_simulated[i, j]
          for i in range(5)
          for j in range(5)
      )
  
      portfolio_volatility = np.sqrt(portfolio_volatility)
  
      return -alpha1 * portfolio_return + alpha2 * portfolio_volatility
  
file_path: nevergrad/examples/raytune_nevergrad_biobjective.py
imports: import nevergrad
  import numpy
  import nevergrad
  from ray import train, tune
  from ray.tune.search.nevergrad import NevergradSearch
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(weight_1, weight_2, weight_3, weight_4, weight_5, alpha1, alpha2) and returns: unknown