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Solve the following python programming problem given the following code:
# Importing th base class structure for log and gradient log of probability density function
from pgmpy.sampling import BaseGradLogPDF

# Base class for user defined continuous factor
from pgmpy.factors.distributions import CustomDistribution

# Defining pdf of a Logistic distribution with mu = 5, s = 2
def logistic_pdf(x):
    power = - (x - 5.0) / 2.0
    return np.exp(power) / (2 * (1 + np.exp(power))**2)

# Calculating log of logistic pdf
def log_logistic(x):
    power = - (x - 5.0) / 2.0
    return power - np.log(2.0) - 2 * np.log(1 + np.exp(power))

# Calculating gradient log of logistic pdf
def grad_log_logistic(x):
    power = - (x - 5.0) / 2.0
    return - 0.5 - (2 / (1 + np.exp(power))) * np.exp(power) * (-0.5)

# Creating a logistic model
logistic_model = CustomDistribution(['x'], logistic_pdf)

# Creating a class using base class for gradient log and log probability density function
class GradLogLogistic(BaseGradLogPDF):

    def __init__(self, variable_assignments, model):
        BaseGradLogPDF.__init__(self, variable_assignments, model)
        self.grad_log, self.log_pdf = self._get_gradient_log_pdf()

    def _get_gradient_log_pdf(self):
        return (grad_log_logistic(self.variable_assignments),
                log_logistic(self.variable_assignments))

# Generating samples using NUTS
sampler = NUTSda(model=logistic_model, grad_log_pdf=GradLogLogistic)
samples = sampler.sample(initial_pos=np.array([0.0]), num_adapt=10000,
                         num_samples=10000)

x = np.linspace(-30, 30, 10000)
y = [logistic_pdf(i) for i in x]
plt.figure(figsize=(8, 8))
plt.hold(1)
plt.plot(x, y, label='real logistic pdf')
plt.hist(samples.values, normed=True, histtype='step', bins=200, label='Samples NUTSda')
plt.legend()
plt.hold(0)
plt.show()
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Support for coustom Models
One can also use HMC, HMCda, NUTS and NUTSda to sample from a user defined model, all one has to do is create class for finding log of probability density function and gradient log of probability density function using base class provided by pgmpy, and give this class as a parameter to grad_log_pdf argument in all of these algorithms (HMC[da], NUTS[da]).
In this example we will define our own logisitcs distribution and use NUTSda to sample from it.
The probability density of logistic distribution is given by:
$$ P(x; \mu, s) = \frac{e^{-\frac{x- \mu}{s}}}{s(1 + e^{-\frac{x - \mu}{s}})^2} $$
Thus the log of this probability density function (potential energy function) can be written as:
$$ log(P(x; \mu, s)) = -\frac{x - \mu}{s} - log(s) - 2 log(1 + e^{-\frac{x - \mu}{s}}) $$
And the gradient of potential energy :
$$ \frac{\partial log(P(x; \mu, s))}{\partial x} = - \frac{1}{s} + \frac{2e^{-\frac{x - \mu}{s}}}{s(1 + e^{-\frac{x - \mu}{s}})}$$