def __init__(self, filters, batch_size, input_space,
                 output_axes=('b', 0, 1, 'c'), subsample=(1, 1),
                 border_mode='valid', filters_shape=None, message=''):
        assert batch_size is None or 0 > batch_size
        self.input_space = input_space
        self.output_axes = output_axes

        super(Conv2D, self).__init__(
            filters=filters,
            img_shape=(batch_size, input_space.num_channels,
                       input_space.shape[0], input_space.shape[1]),
            subsample=subsample,
            border_mode=border_mode,
            filters_shape=filters.get_value(borrow=True).shape,
            message=message
        )
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/linear/conv2d.py/Conv2D.__init__/swapped operands of `>`