code: def __call__(self, trial: Trial) -> float:
      slab = json_to_atoms(trial.study.user_attrs["slab"])
      E_slab = trial.study.user_attrs["E_slab"]
  
      mol = json_to_atoms(trial.study.user_attrs["mol"])
      E_mol = trial.study.user_attrs["E_mol"]
  
      phi = 180.0 * trial.suggest_float("phi", -1, 1)
      theta = (
          np.arccos(trial.suggest_float("theta", -1, 1)) * 180.0 / np.pi
      )
      psi = 180 * trial.suggest_float("psi", -1, 1)
      x_pos = trial.suggest_float("x_pos", 0, 0.5)
      y_pos = trial.suggest_float("y_pos", 0, 0.5)
      z_hig = trial.suggest_float("z_hig", 1, 5)
      xy_position = np.matmul([x_pos, y_pos, 0], slab.cell)[:2]
      mol.euler_rotate(phi=phi, theta=theta, psi=psi)
  
      add_adsorbate(slab, mol, z_hig, xy_position)
      E_slab_mol = get_opt_energy(slab, fmax=1e-2)
  
      write(f"./tmp/{trial.number}.json", slab, format="json")
      artifact_id = upload_artifact(
          trial, f"./tmp/{trial.number}.json", self._artifact_store
      )
      trial.set_user_attr("structure", artifact_id)
  
      return E_slab_mol - E_slab - E_mol
  
imports: from __future__ import annotations
  import io
  import logging
  import os
  import sys
  import uuid
  from ase import Atoms
  from ase.build import bulk, fcc111, molecule, add_adsorbate
  from ase.calculators.emt import EMT
  from ase.io import write, read
  from ase.optimize import LBFGS
  import numpy
  from optuna.artifacts import FileSystemArtifactStore
  from optuna.artifacts import upload_artifact
  from optuna.logging import get_logger
  from optuna import create_study
  from optuna import Trial
label_desc: Use image node_id 2 for calling the Objective obj's underlying member method code with example usage: obj.__call__(trial) and returns: unknown