code: def spilu(
      A,
      drop_tol=None,
      fill_factor=None,
      drop_rule=None,
      permc_spec=None,
      diag_pivot_thresh=None,
      relax=None,
      panel_size=None,
      options={},
  ):
      """Computes the incomplete LU decomposition of a sparse square matrix.
  
      Args:
          A (cupyx.scipy.sparse.spmatrix): Sparse matrix to factorize.
          drop_tol (float): (For further augments, see
              :func:`scipy.sparse.linalg.spilu`)
          fill_factor (float):
          drop_rule (str):
          permc_spec (str):
          diag_pivot_thresh (float):
          relax (int):
          panel_size (int):
          options (dict):
  
      Returns:
          cupyx.scipy.sparse.linalg.SuperLU:
              Object which has a ``solve`` method.
  
      Note:
          This function computes incomplete LU decomposition of a sparse matrix
          on the CPU using `scipy.sparse.linalg.spilu` (unless you set
          ``fill_factor`` to ``1``). Therefore, incomplete LU decomposition is
          not accelerated on the GPU. On the other hand, the computation of
          solving linear equations using the ``solve`` method, which this
          function returns, is performed on the GPU.
  
          If you set ``fill_factor`` to ``1``, this function computes incomplete
          LU decomposition on the GPU, but without fill-in or pivoting.
  
      .. seealso:: :func:`scipy.sparse.linalg.spilu`
      """
      from cupyx import cusparse
  
      if not scipy_available:
          raise RuntimeError("scipy is not available")
      if not sparse.isspmatrix(A):
          raise TypeError("A must be cupyx.scipy.sparse.spmatrix")
      if A.shape[0] != A.shape[1]:
          raise ValueError(
              "A must be a square matrix (A.shape: {})".format(A.shape)
          )
      if A.dtype.char not in "fdFD":
          raise TypeError("Invalid dtype (actual: {})".format(A.dtype))
  
      if fill_factor == 1:
          # Computes ILU(0) on the GPU using cuSparse functions
          if not sparse.isspmatrix_csr(A):
              a = A.tocsr()
          else:
              a = A.copy()
          cusparse.csrilu02(a)
          return CusparseLU(a)
  
      a = A.get().tocsc()
      a_inv = scipy.sparse.linalg.spilu(
          a,
          fill_factor=fill_factor,
          drop_tol=drop_tol,
          drop_rule=drop_rule,
          permc_spec=permc_spec,
          diag_pivot_thresh=diag_pivot_thresh,
          relax=relax,
          panel_size=panel_size,
          options=options,
      )
      return SuperLU(a_inv)
  
imports: import numpy
  import cupy
  from cupy import cublas
  from cupy.cuda import device
  from cupy.cuda import runtime
  from cupy.linalg import _util
  from cupyx.scipy import sparse
  from cupyx.scipy.sparse.linalg import _interface
  from cupyx.scipy.sparse.linalg._iterative import _make_system
  import warnings
label_desc: Use image node_id 8 for calling a global function with example usage: spilu(A, drop_tol, fill_factor, drop_rule, permc_spec, diag_pivot_thresh, relax, panel_size, options) and returns: SuperLU, CusparseLU