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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 cov(m, y=None, rowvar=1, bias=0, ddof=None, weights=None, repeat_weights=0):
 """ Estimate a covariance matrix, given data. Covariance indicates the level to which two variables vary together. then the covariance matrix element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance of :math:`x_i`. Parameters m : array_like A 1-D or 2-D array containing multiple variables and observations. Each row of `m` represents a variable, and each column a single observation of all those variables. Also see `rowvar` below. y : array_like, optional An additional set of variables and observations. `y` has the same form as that of `m`. rowvar : int, optional If `rowvar` is non-zero (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations. bias : int, optional Default normalization is by ``(N - 1)``, where ``N`` is the number of observations given (unbiased estimate). If `bias` is 1, then normalization is by ``N``. These values can be overridden by using the keyword ``ddof`` in numpy versions >= 1.5. ddof : int, optional .. versionadded:: 1.5 If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is the number of observations; this overrides the value implied by ``bias``. The default value is ``None``. weights : array-like, optional A 1-D array of weights with a length equal to the number of observations. repeat_weights : int, optional The default treatment of weights in the weighted covariance is to first normalize them to unit sum and use the biased weighted covariance equation. If `repeat_weights` is 1 then the weights must represent an integer number of occurrences of each observation and both a biased and unbiased weighted covariance is defined because the total sample size can be determined. Returns ------- out : ndarray The covariance matrix of the variables. See Also -------- corrcoef : Normalized covariance matrix Examples -------- Consider two variables, :math:`x_0` and :math:`x_1`, which correlate perfectly, but in opposite directions: array([[0, 1, 2], [2, 1, 0]]) Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance matrix shows this clearly: array([[ 1., -1.], [-1., 1.]]) Note that element :math:`C_{0,1}`, which shows the correlation between :math:`x_0` and :math:`x_1`, is negative. Further, note how `x` and `y` are combined: [[ 11.71 -4.286 ] [ -4.286 2.14413333]] [[ 11.71 -4.286 ] [ -4.286 2.14413333]] 11.71 """

    import numpy as np
    # Check inputs
    if ddof is not None and ddof != int(ddof):
        raise ValueError(
            "ddof must be integer")

    X = np.array(m, ndmin=2, dtype=float)
    if X.size == 0:
        # handle empty arrays
        return np.array(m)
    if X.shape[0] == 1:
        rowvar = 1
    if rowvar:
        axis = 0
        tup = (slice(None), np.newaxis)
    else:
        axis = 1
        tup = (np.newaxis, slice(None))

    if y is not None:
        y = np.array(y, copy=False, ndmin=2, dtype=float)
        X = np.concatenate((X, y), axis)

    if ddof is None:
        if bias == 0:
            ddof = 1
        else:
            ddof = 0

    if weights is not None:
        weights = np.array(weights, dtype=float)
        weights_sum = weights.sum()
        if weights_sum <= 0:
            raise ValueError(
                "sum of weights is non-positive")
        X -= np.average(X, axis=1-axis, weights=weights)[tup]

        if repeat_weights:
            # each weight represents a number of repetitions of an observation
            # the total sample size can be determined in this case and we have
            # both an unbiased and biased weighted covariance
            fact = weights_sum - ddof
        else:
            # normalize weights so they sum to unity
            weights /= weights_sum
            # unbiased weighted covariance is not defined if the weights are
            # not integral frequencies (repeat-type)
            fact = (1. - np.power(weights, 2).sum())
    else:
        weights = 1
        X -= X.mean(axis=1-axis)[tup]
        if rowvar:
            N = X.shape[1]
        else:
            N = X.shape[0]
        fact = float(N - ddof)

    if not rowvar:
        return (np.dot(weights * X.T, X.conj()) / fact).squeeze()
    else:
        return (np.dot(weights * X, X.T.conj()) / fact).squeeze()