code: def _is_c_order(row, col):
      """Check if a coo matrix with given row and col is c_order"""
      if row.shape != col.shape:
          raise ValueError("shape of row and col must be the same.")
      if row.ndim != 1:
          for i in range(row.shape[0]):
              if not _is_c_order(row[i], col[i]):
                  return False
          return True
      xp = backend.get_array_module(row)
      _row = row[col >= 0]
      _col = col[row >= 0]
      if _row[_row < 0].size > 0 or _col[_col < 0].size:
          raise ValueError("invalid index combination of row and col.")
      if _row.shape[0] <= 1:
          return True
      row_diff = xp.zeros(_row.shape, dtype=_row.dtype)
      row_diff[1:] = _row[1:] - _row[:-1]
      if xp.amin(row_diff) < 0:
          return False
      col_diff = xp.zeros(_col.shape, dtype=_col.dtype)
      col_diff[1:] = _col[1:] - _col[:-1]
      col_diff[(row_diff > 0)] = 0
      return xp.amin(col_diff) >= 0
  
file_path: chainer/chainer/utils/sparse.py
imports: import numpy
  import chainer
  from chainer import backend
  from chainer import cuda
label_desc: Use image node_id 4 for calling a global function with example usage: _is_c_order(row, col) and returns: unknown, True, True, False, False