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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 power_chisq_at_points_from_precomputed(corr, snr, snr_norm, bins, indices):
 """Calculate the chisq timeseries from precomputed values for only select points. This function calculates the chisq at each point by explicitly time shifting and summing each bin. No FFT is involved. Parameters corr: FrequencySeries The product of the template and data in the frequency domain. snr: numpy.ndarray The unnormalized array of snr values at only the selected points in `indices`. snr_norm: float The normalization of the snr (EXPLAINME : refer to Findchirp paper?) bins: List of integers The edges of the equal power bins indices: Array The indices where we will calculate the chisq. These must be relative to the given `corr` series. Returns ------- chisq: Array An array containing only the chisq at the selected points. """

    num_bins = len(bins) - 1
    chisq = shift_sum(corr, indices, bins) # pylint:disable=assignment-from-no-return
    return (chisq * num_bins - (snr.conj() * snr).real) * (snr_norm ** 2.0)