The code below gives the algorithm for Gaussian Elimination with scaled partial pivoting. Below the code, summarize what each block in the code is doing. def GESPP(A,b): n=len(A) # number of rows of A m=len(A[1]) # number of cols of A 1=[@]*n S=[@]*n ###### Block 1 ##### for i in range(n): 1[i]=i smax = for j in range(n): smax=max(smax, abs (A[i][5])) S[i]=smax ###### Block 2 ##### for kin range(n-1): max=0 for i in range(k,n): r = abs(A[1[1]][k]/[1[i]]) if r>rmax: max=r j=i 1[k],[5]=1[3], 1[k] for i in range(k+1, n): xmult = A[1[1]][k]/A[1[k]][k] #A[1[i]][K]=xmult for j in range(k+1,n): A[1[i]][j]=A[1[i]][j]-(xmult)*A[1[k]][j] b[1[i]]=b[1[1]]-(mult)* b[1[k]] ###### Block 3 ##### b[1[n-1]]=b[1[n-1]]/A[1[n-1]][n-1] for i in range(n-2,-1,-1): sum=b[1[1]] for j in range (i+1,n): sum=sum-A[1[1]][j]*b[1[i]] b[1[i]]=sum/A[1[i]][i] **#### Block 4 ##### X=[@]*n for i in range(n): x[i]=b[1[i]] print(x) Below, summarize what each block of the code above is doing. Block 1: Block 2: Block 3: Block 4: The code below gives the algorithm for Gaussian Elimination with scaled partial pivoting. Below the code, summarize what each block in the code is doing. def GESPP(A,b): n=len(A) # number of rows of A m=len(A[1]) # number of cols of A 1=[@]*n S=[@]*n ###### Block 1 ##### for i in range(n): 1[i]=i smax = for j in range(n): smax=max(smax, abs (A[i][5])) S[i]=smax ###### Block 2 ##### for kin range(n-1): max=0 for i in range(k,n): r = abs(A[1[1]][k]/[1[i]]) if r>rmax: max=r j=i 1[k],[5]=1[3], 1[k] for i in range(k+1, n): xmult = A[1[1]][k]/A[1[k]][k] #A[1[i]][K]=xmult for j in range(k+1,n): A[1[i]][j]=A[1[i]][j]-(xmult)*A[1[k]][j] b[1[i]]=b[1[1]]-(mult)* b[1[k]] ###### Block 3 ##### b[1[n-1]]=b[1[n-1]]/A[1[n-1]][n-1] for i in range(n-2,-1,-1): sum=b[1[1]] for j in range (i+1,n): sum=sum-A[1[1]][j]*b[1[i]] b[1[i]]=sum/A[1[i]][i] **#### Block 4 ##### X=[@]*n for i in range(n): x[i]=b[1[i]] print(x) Below, summarize what each block of the code above is doing. Block 1: Block 2: Block 3: Block 4