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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 inertia(X,y,samples=False):
 """ return the within-class squared distance from the centroid"""

    # pdb.set_trace()
    if samples:
        # return within-class distance for each sample
        inertia = np.zeros(y.shape)
        for label in np.unique(y):
            inertia[y==label] = (X[y==label] - np.mean(X[y==label])) ** 2

    else: # return aggregate score
        inertia = 0
        for i,label in enumerate(np.unique(y)):
            inertia += np.sum((X[y==label] - np.mean(X[y==label])) ** 2)/len(y[y==label])
        inertia = inertia/len(np.unique(y))

    return inertia