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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 walk(chains, start, end, step):
 """ Calculates Gelman-Rubin conervergence statistic along chains of data. This function will advance along the chains and calculate the statistic for each step. Parameters chains : iterable An iterable of numpy.array instances that contain the samples for each chain. Each chain has shape (nparameters, niterations). start : float Start index of blocks to calculate all statistics. end : float Last index of blocks to calculate statistics. step : float Step size to take for next block. Returns ------- starts : numpy.array 1-D array of start indexes of calculations. ends : numpy.array 1-D array of end indexes of caluclations. stats : numpy.array Array with convergence statistic. It has shape (nparameters, ncalculations). """


    # get number of chains, parameters, and iterations
    chains = numpy.array(chains)
    _, nparameters, _ = chains.shape

    # get end index of blocks
    ends = numpy.arange(start, end, step)
    stats = numpy.zeros((nparameters, len(ends)))

    # get start index of blocks
    starts = numpy.array(len(ends) * [start])

    # loop over end indexes and calculate statistic
    for i, e in enumerate(ends):
        tmp = chains[:, :, 0:e]
        stats[:, i] = gelman_rubin(tmp)

    return starts, ends, stats