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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 estimate_mutual_information(x, y):
 """Estimate the mutual information of two datasets. Mutual information is a measure of dependence between two datasets and is calculated as: $I(x;y) = H(x) + H(y) - H(x,y)$ Where H(x) is the Shannon entropy of x. For continuous datasets, adapts the Kraskov Estimator [1] for mutual information. Args: x (array-like):
 An array with shape (n_samples, n_features_x) y (array-like):
 An array with shape (n_samples, n_features_y) Returns: float: A floating point number representing the mutual information of x and y. This calculation is *exact* for entirely discrete datasets and *approximate* if there are continuous columns present. References: .. [1] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual information". Phys. Rev. E 69, 2004. """

    xy = np.concatenate((x, y), axis=1)
    epsilon = _calculate_epsilon(xy)
    h_x = estimate_entropy(x, epsilon)
    h_y = estimate_entropy(y, epsilon)
    h_xy = estimate_entropy(xy, epsilon)
    return max(0, h_x + h_y - h_xy)