code: def test_large_input():
      available_bytes, _ = cp.cuda.runtime.memGetInfo()
      # 15 GB
      required_bytes = 1.5e10
      if available_bytes < required_bytes:
          pytest.skip("Not enough memory on this device")
      n = 1000
      m = ((1 << 31) + n - 1) // n
      assert np.log2(m * n) > 31
      X = cp.ones((m, n), dtype=np.float32)
      y = cp.ones(m)
      w = cp.ones(m)
      dmat = xgb.QuantileDMatrix(X, y, weight=w)
      booster = xgb.train(
          {"tree_method": "gpu_hist", "max_depth": 1}, dmat, 1
      )
      del y
      booster.inplace_predict(X)
  
imports: import cupy
  import numpy
  import pytest
  import xgboost
label_desc: Use image node_id 1 for calling a global function with example usage: test_large_input() without return types