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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 explore_learning(self, d, x, mu_start=0, mu_end=1., steps=100, ntrain=0.5, epochs=1, criteria="MSE", target_w=False):
 """ Test what learning rate is the best. **Args:** * `d` : desired value (1 dimensional array) * `x` : input matrix (2-dimensional array). Rows are samples, columns are input arrays. **Kwargs:** * `mu_start` : starting learning rate (float) * `mu_end` : final learning rate (float) * `steps` : how many learning rates should be tested between `mu_start` and `mu_end`. * `ntrain` : train to test ratio (float), default value is 0.5 (that means 50% of data is used for training) * `epochs` : number of training epochs (int), default value is 1. This number describes how many times the training will be repeated on dedicated part of data. * `criteria` : how should be measured the mean error (str), default value is "MSE". * `target_w` : target weights (str or 1d array), default value is False. If False, the mean error is estimated from prediction error. If an array is provided, the error between weights and `target_w` is used. **Returns:** * `errors` : mean error for tested learning rates (1 dimensional array). * `mu_range` : range of used learning rates (1d array). Every value corresponds with one value from `errors` """

        mu_range = np.linspace(mu_start, mu_end, steps)
        errors = np.zeros(len(mu_range))
        for i, mu in enumerate(mu_range):
            # init
            self.init_weights("zeros")
            self.mu = mu
            # run
            y, e, w = self.pretrained_run(d, x, ntrain=ntrain, epochs=epochs)
            if type(target_w) != bool:
                errors[i] = get_mean_error(w[-1]-target_w, function=criteria)
            else:
                errors[i] = get_mean_error(e, function=criteria)
        return errors, mu_range