code: def test_cpu_and_gpu_work():
      # If compiled appropriately, the same installation will support both GPU and CPU.
      X, y = load_breast_cancer(return_X_y=True)
      data = lgb.Dataset(X, y)
  
      params_cpu = {
          "verbosity": -1,
          "num_leaves": 31,
          "objective": "binary",
          "device": "cpu",
      }
      cpu_bst = lgb.train(params_cpu, data, num_boost_round=10)
      cpu_score = log_loss(y, cpu_bst.predict(X))
  
      params_gpu = params_cpu.copy()
      params_gpu["device"] = "gpu"
      # Double-precision floats are only supported on x86_64 with PoCL
      params_gpu["gpu_use_dp"] = platform.machine() == "x86_64"
      gpu_bst = lgb.train(params_gpu, data, num_boost_round=10)
      gpu_score = log_loss(y, gpu_bst.predict(X))
  
      rel = 1e-6 if params_gpu["gpu_use_dp"] else 1e-4
      assert cpu_score == pytest.approx(gpu_score, rel=rel)
      assert gpu_score < 0.242
  
imports: import os
  import platform
  import pytest
  from sklearn.metrics import log_loss
  import lightgbm
  from .utils import load_breast_cancer
label_desc: Use image node_id 1 for calling a global function with example usage: test_cpu_and_gpu_work() without return types