human
Solve the following python programming problem given the following code:
# Get true initial population size and carrying capacity
f_0 = parameters[0]
k = parameters[2]

# Forget about f_0 and k (we won't infer those parameters)
true_parameters = np.hstack([parameters[1:2], parameters[3:]])

# Create a wrapper around the logistic model
class Model(pints.ForwardModel):
    def __init__(self, f_0, k):
        self._k = k
        self._model = pints.toy.LogisticModel(initial_population_size=f_0)

    def simulate(self, parameters, times):
        return self._model.simulate(parameters=[parameters[0], self._k], times=times)

    def n_parameters(self):
        return 1

# Create an inverse problem which links the logistic growth model to the data
problem = pints.SingleOutputProblem(model=Model(f_0=f_0, k=k), times=data[0, :], values=data[1, :])

# Create the constant and multiplicative Gaussian error log-likelihood
log_likelihood = pints.ConstantAndMultiplicativeGaussianLogLikelihood(problem)

# Create uniform priors for [r, sigma_base, eta, sigma_rel]
log_prior_r = pints.UniformLogPrior(
    [0.005],
    [0.02]
)
log_prior_sigma_base = pints.UniformLogPrior(
    [1],
    [20]
)
log_prior_eta = pints.UniformLogPrior(
    [0.5],
    [1.5]
)
log_prior_sigma_rel = pints.UniformLogPrior(
    [0.001],
    [1]
)
log_prior = pints.ComposedLogPrior(
    log_prior_r, log_prior_sigma_base, log_prior_eta, log_prior_sigma_rel)

# Create posterior (free eta)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)

# Fix eta
eta = true_parameters[2]
log_posterior_fixed_eta = LogPosteriorWrapper(log_posterior, eta)
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python code to fix eta
.

Create log-posterior with fixed $\eta $