code: def run_inplace_predict_cupy(self, device: int) -> None:
      import cupy as cp
  
      cp.cuda.runtime.setDevice(device)
      rows = 1000
      cols = 10
      missing = 11  # set to integer for testing
  
      cp_rng = cp.random.RandomState(1994)
      cp.random.set_random_state(cp_rng)
  
      X = cp.random.randn(rows, cols)
      missing_idx = [i for i in range(0, cols, 4)]
      X[:, missing_idx] = missing  # set to be missing
      y = cp.random.randn(rows)
  
      dtrain = xgb.DMatrix(X, y)
  
      booster = xgb.train(
          {"tree_method": "hist", "device": f"cuda:{device}"},
          dtrain,
          num_boost_round=10,
      )
  
      test = xgb.DMatrix(X[:10, ...], missing=missing)
      predt_from_array = booster.inplace_predict(
          X[:10, ...], missing=missing
      )
      predt_from_dmatrix = booster.predict(test)
      cp.testing.assert_allclose(predt_from_array, predt_from_dmatrix)
  
      def predict_dense(x):
          cp.cuda.runtime.setDevice(device)
          inplace_predt = booster.inplace_predict(x)
          d = xgb.DMatrix(x)
          copied_predt = cp.array(booster.predict(d))
          return cp.all(copied_predt == inplace_predt)
  
      # Don't do this on Windows, see issue #5793
      if sys.platform.startswith("win"):
          pytest.skip(
              "Multi-threaded in-place prediction with cuPy is not working on Windows"
          )
      for i in range(10):
          run_threaded_predict(X, rows, predict_dense)
  
      base_margin = cp_rng.randn(rows)
      self.run_inplace_base_margin(
          device, booster, dtrain, X, base_margin
      )
  
      # Create a wide dataset
      X = cp_rng.randn(100, 10000)
      y = cp_rng.randn(100)
  
      missing_idx = [i for i in range(0, X.shape[1], 16)]
      X[:, missing_idx] = missing
      reg = xgb.XGBRegressor(
          tree_method="hist",
          n_estimators=8,
          missing=missing,
          device=f"cuda:{device}",
      )
      reg.fit(X, y)
  
      reg.set_params(device=f"cuda:{device}")
      gpu_predt = reg.predict(X)
      reg = reg.set_params(device="cpu")
      cpu_predt = reg.predict(cp.asnumpy(X))
      np.testing.assert_allclose(gpu_predt, cpu_predt, atol=1e-6)
      cp.cuda.runtime.setDevice(0)
  
imports: import sys
  from copy import copy
  import numpy
  import pytest
  from hypothesis import assume, given, settings, strategies
  import xgboost
  from xgboost import testing
  from xgboost.compat import PANDAS_INSTALLED
  from test_predict import run_predict_leaf
  from test_predict import run_threaded_predict
label_desc: Use image node_id 6 for calling the TestGPUPredict obj's underlying member method code with example usage: obj.run_inplace_predict_cupy(device) and returns: cp