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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 cluster_rows_in_roi(self, roi_mask=None, num_clusters_per_roi=5, metric='minkowski'):
 """Clusters the data within all the ROIs specified in a mask. Parameters roi_mask : ndarray or None volumetric mask defining the list of ROIs, with a label for each voxel. This must be the same size in all dimensions except the fixed_dim i.e. if you were making a Carpet from an fMRI image of size 125x125x90x400 fixing the 4th dimension (of size 400), then roi_mask must be of size 125x125x90. num_clusters_per_roi : int number of clusters (n) to form each ROI specified in the roi_mask if n (say 20) is less than number of voxels per a given ROI (say 2000), then data from approx. 2000/20=100 voxels would summarized (averaged by default), into a single cluster. So if the ROI mask had m ROIs (say 10), then the final clustered carpet would have m*n rows (200), regardless of the number of voxels in the 3D image. metric : str distance metric for the hierarchical clustering algorithm; default : 'minkowski' Options: anything accepted by `scipy.spatial.distance.pdist`, which can be: ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’. """


        self._set_roi_mask(roi_mask)

        try:
            clusters = [self._summarize_in_roi(self.roi_mask == label,
                                               num_clusters_per_roi,
                                               metric=metric) for label in self.roi_list]
            self.clustered_carpet = np.vstack(clusters)
        except:
            print('unable to produce the clustered carpet - exception:')
            traceback.print_exc()
            self._carpet_clustered = False
        else:
            self._carpet_clustered = True