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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 aligned_covariance(fit, type='noise'):
 """ Covariance rescaled so that eigenvectors sum to 1 and rotated into data coordinates from PCA space """

    cov = fit._covariance_matrix(type)
    # Rescale eigenvectors to sum to 1
    cov /= N.linalg.norm(cov)
    return dot(fit.axes,cov)