code: def salsa_compute_negative_validation_error(
      hyperparams,
      num_tr_data_to_use,
      X_tr=None,
      Y_tr=None,
      X_va=None,
      Y_va=None,
      *args,
      **kwargs,
  ):
      """Computes the negative validation error."""
      # Check if training data is provided
      if X_tr is None:
          X_tr = ENERGY_DATA["train"]["x"][:MAX_TR_DATA_SIZE]
          Y_tr = ENERGY_DATA["train"]["y"][:MAX_TR_DATA_SIZE]
          X_va = ENERGY_DATA["vali"]["x"][:MAX_VA_DATA_SIZE]
          Y_va = ENERGY_DATA["vali"]["y"][:MAX_VA_DATA_SIZE]
      # Unpack the hyperparams
      kernel_type = hyperparams[0]
      add_order = hyperparams[1]
      kernel_scale = 10 ** hyperparams[2]
      bandwidths = 10 ** np.array(hyperparams[3])
      l2_reg = 10 ** hyperparams[4]
      # call the salsa function
      vali_err = salsa_train_and_validate(
          X_tr,
          Y_tr,
          X_va,
          Y_va,
          kernel_type,
          add_order,
          kernel_scale,
          bandwidths,
          l2_reg,
          num_tr_data_to_use,
          *args,
          **kwargs,
      )
      return -vali_err
  
imports: import numpy
  import pickle
  from salsa_estimator import salsa_train_and_validate
label_desc: Use image node_id 2 for calling a global function with example usage: salsa_compute_negative_validation_error(hyperparams, num_tr_data_to_use, X_tr, Y_tr, X_va, Y_va) and returns: unknown