SystemError: <Boost.Python.function object > returned NULL without setting an error

[Basharat Ali]

 I am doing   Multilabel classification on PASCAL using python data-layers   in python where I encounter the following error

File "pycaffe/layers/pascal_multilabel_datalayers.py", line 106

    def load_next_image(self):
      ^
SyntaxError: invalid syntax

---------------------------------------------------------------------------
SystemError                               Traceback (most recent call last)
<ipython-input-12-fa2cf1b2cc39> in <module>()
----> 1 solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))
      2 solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')
      3 solver.test_nets[0].share_with(solver.net)
      4 solver.step(1)

SystemError: <Boost.Python.function object > returned NULL without setting an error

 and  the function in

pycaffe/layers/pascal_multilabel_datalayers.py

is  as following

def load_next_image(self):
        """
        Load the next image in a batch.
        """
        # Did we finish an epoch?
        if self._cur == len(self.indexlist):
            self._cur = 0
            shuffle(self.indexlist)

        # Load an image
        index = self.indexlist[self._cur]  # Get the image index
        image_file_name = index + '.jpg'
        im = np.asarray(Image.open(
            osp.join(self.pascal_root, 'JPEGImages', image_file_name)))
        im = scipy.misc.imresize(im, self.im_shape)  # resize

        # do a simple horizontal flip as data augmentation
        flip = np.random.choice(2)*2-1
        im = im[:, ::flip, :]

        # Load and prepare ground truth
        multilabel = np.zeros(20).astype(np.float32)
        anns = load_pascal_annotation(index, self.pascal_root)
        for label in anns['gt_classes']:
            # in the multilabel problem we don't care how MANY instances
            # there are of each class. Only if they are present.
            # The "-1" is b/c we are not interested in the background
            # class.
            multilabel[label - 1] = 1

        self._cur += 1
        return self.transformer.preprocess(im), multilabel