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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 partial_derivative_mu(mu, sigma, low, high, data):
 """The partial derivative with respect to the mean. Args: mu (float):
 the mean of the truncated normal sigma (float):
 the std of the truncated normal low (float):
 the lower truncation bound high (float):
 the upper truncation bound data (ndarray):
 the one dimension list of data points for which we want to calculate the likelihood Returns: float: the partial derivative evaluated at the given point """

        pd_mu = np.sum(data - mu) / sigma ** 2
        pd_mu -= len(data) * ((norm.pdf(low, mu, sigma) - norm.pdf(high, mu, sigma))
                              / (norm.cdf(high, mu, sigma) - norm.cdf(low, mu, sigma)))
        return -pd_mu