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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 assemble_covariance(error, correlation):
 """ This takes an error vector and a correlation matrix and assembles the covariance """


    covariance = []
    for n in range(0, len(error)):
        covariance.append([])
        for m in range(0, len(error)):
            covariance[n].append(correlation[n][m]*error[n]*error[m])
    return _n.array(covariance)