human
Solve the following python programming problem given the following code:
# Hint

from thinkbayes2 import Suite, Joint, EvalWeibullPdf

class LightBulb(Suite, Joint):
    
    def Likelihood(self, data, hypo):
        lam, k = hypo
        x = data
        like = 1
        return like

# Solution goes here

from itertools import product

lams = np.linspace(0.001, 6, 101)
ks = np.linspace(0.001, 8, 101)

suite = LightBulb(product(lams, ks))

suite.UpdateSet(data)

thinkplot.Contour(suite)

pmf_lam = suite.Marginal(0)
thinkplot.Pdf(pmf_lam)
pmf_lam.Mean()

pmf_k = suite.Marginal(1)
thinkplot.Pdf(pmf_k)
pmf_k.Mean()
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Exercise: Write a class called LightBulb that inherits from Suite and provides a Likelihood function that takes an observed lifespan as data and a tuple, (lam, k), as a hypothesis.  It should return a likelihood proportional to the probability of the observed lifespan in a Weibull distribution with the given parameters.
Test your method by creating a LightBulb object with an appropriate prior and update it with the data above.
Plot the posterior distributions of lam and k.  As the sample size increases, does the posterior distribution converge on the values of lam and k used to generate the sample?