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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def data_labels(self, image, fudged_image, segments, classifier_fn, num_samples, batch_size=10):
 """Generates images and predictions in the neighborhood of this image. Args: image: 3d numpy array, the image fudged_image: 3d numpy array, image to replace original image when superpixel is turned off segments: segmentation of the image classifier_fn: function that takes a list of images and returns a matrix of prediction probabilities num_samples: size of the neighborhood to learn the linear model batch_size: classifier_fn will be called on batches of this size. Returns: A tuple (data, labels), where: data: dense num_samples * num_superpixels labels: prediction probabilities matrix """

        n_features = np.unique(segments).shape[0]
        data = self.random_state.randint(0, 2, num_samples * n_features)\
            .reshape((num_samples, n_features))
        labels = []
        data[0, :] = 1
        imgs = []
        for row in data:
            temp = copy.deepcopy(image)
            zeros = np.where(row == 0)[0]
            mask = np.zeros(segments.shape).astype(bool)
            for z in zeros:
                mask[segments == z] = True
            temp[mask] = fudged_image[mask]
            imgs.append(temp)
            if len(imgs) == batch_size:
                preds = classifier_fn(np.array(imgs))
                labels.extend(preds)
                imgs = []
        if len(imgs) > 0:
            preds = classifier_fn(np.array(imgs))
            labels.extend(preds)
        return data, np.array(labels)