The single linkage method in scipy package uses the minimum distance between two observations as the linkage criterion. This is also known as "nearest neighbor" linkage. In other words, the distance between two clusters is defined as the distance between their closest pairs of observations, where distance is a metric defined over the observations. In the scipy package, the linkage method can be specified as an input parameter, and the default linkage criterion is euclidean distance. However, the linkage function also allows to use other distance metric such as 'minkowski', 'cityblock' (Manhattan distance), 'seuclidean', 'cosine', 'correlation' etc. In this question, you are asked to implement a new distance metric function as follows: d(x,y)=L2.5={i,j(xiyj)2.5}2.51 Where xi and yj are two observations. Implement the hierarchical clustering algorithm using L2.5 distance metric function on the generated blobs data, and plot the dendrogram. Include your code in the starter code below: import numpy as np import matplotlib.pyplot as plt from scipy.spatial.distance import pdist, squareform from scipy. cluster.hierarchy import linkage, dendrogram \# Generate random blobs of data data, labels = make_blobs(n_samples =200, centers=3, random_state=0, cluster_std=1) \#\#\# YOUR CODE GOES HERE \#\#\#