code: def _csr_row_index(Ax, Aj, Ap, rows):
      """Populate indices and data arrays from the given row index
      Args:
          Ax (cupy.ndarray): data array from input sparse matrix
          Aj (cupy.ndarray): indices array from input sparse matrix
          Ap (cupy.ndarray): indptr array from input sparse matrix
          rows (cupy.ndarray): index array of rows to populate
      Returns:
          Bx (cupy.ndarray): data array of output sparse matrix
          Bj (cupy.ndarray): indices array of output sparse matrix
          Bp (cupy.ndarray): indptr array for output sparse matrix
      """
      row_nnz = cupy.diff(Ap)
      Bp = cupy.empty(rows.size + 1, dtype=Ap.dtype)
      Bp[0] = 0
      cupy.cumsum(row_nnz[rows], out=Bp[1:])
      nnz = int(Bp[-1])
  
      out_rows = _csr_indptr_to_coo_rows(nnz, Bp)
  
      Bj, Bx = _csr_row_index_ker(out_rows, rows, Ap, Aj, Ax, Bp)
      return Bx, Bj, Bp
  
imports: import cupy
  from cupy import _core
  from cupyx.scipy.sparse._base import isspmatrix
  from cupyx.scipy.sparse._base import spmatrix
  from cupy.cuda import device
  from cupy.cuda import runtime
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: _csr_row_index(Ax, Aj, Ap, rows) and returns: Bx,  Bj,  Bp