code: def test_chunking(self):
      # If the observed data comes from a polynomial, then the interpolant
      # should be able to reproduce the polynomial exactly, provided that
      # `degree` is sufficiently high.
      rng = _np.random.RandomState(0)
      seq = Halton(2, scramble=False, seed=rng)
      degree = 3
  
      largeN = 1000 + 33
      # this is large to check that chunking of the RBFInterpolator is tested
      x = cp.asarray(seq.random(50))
      xitp = cp.asarray(seq.random(largeN))
  
      P = _vandermonde(x, degree)
      Pitp = _vandermonde(xitp, degree)
  
      poly_coeffs = cp.asarray(rng.normal(0.0, 1.0, P.shape[1]))
  
      y = P.dot(poly_coeffs)
      yitp1 = Pitp.dot(poly_coeffs)
      interp = cupyx.scipy.interpolate.RBFInterpolator(
          x, y, degree=degree
      )
      ce_real = interp._chunk_evaluator
  
      def _chunk_evaluator(*args, **kwargs):
          kwargs.update(memory_budget=100)
          return ce_real(*args, **kwargs)
  
      # monkeypatch.setattr(interp, '_chunk_evaluator', _chunk_evaluator)
      interp._chunk_evaluator = _chunk_evaluator
      yitp2 = interp(xitp)
      testing.assert_allclose(yitp1, yitp2, atol=1e-8)
  
imports: import pickle
  import pytest
  import warnings
  import numpy
  from numpy.linalg import LinAlgError
  import cupy
  import cupyx
  from cupy import testing
  import cupyx.scipy.interpolate
  from cupyx.scipy.interpolate._rbfinterp import _AVAILABLE, _SCALE_INVARIANT, _NAME_TO_MIN_DEGREE, NAME_TO_FUNC, _monomial_powers, polynomial_matrix, kernel_matrix
label_desc: Use image node_id 4 for calling the TestRBFInterpolatorNeighborsNone obj's underlying member method code with example usage: obj.test_chunking() and returns: ce_real