Follow the instructions in the screenshots provided carefully to implement the code below in python 3.10 or later, please.   def pi_monte_carlo(n) :     """Computes and returns an estimation of pi     using Monte Carlo simulation.     Keyword arguments:     n - The number of samples.     """     pass def pi_parallel_monte_carlo(n, p=4) :     """Computes and returns an estimation of pi     using a parallel Monte Carlo simulation.     Keyword arguments:     n - The total number of samples.     p - The number of processes to use.     """     # You can distribute the work across p     # processes by having each process     # call the sequential version, where     # those calls divide the n samples across     # the p calls.     # Once those calls return, simply average     # the p partial results and return that average.     pass def generate_table() :     """This function should generate and print a table     of results to demonstrate that both versions     compute increasingly accurate estimations of pi     as n is increased.  It should use the following     values of n = {12, 24, 48, ..., 12582912}. That is,     the first value of n is 12, and then each subsequent     n is 2 times the previous.  The reason for starting at 12     is so that n is always divisible by 1, 2, 3, and 4.     The first     column should be n, the second column should     be the result of calling piMonteCarlo(n), and you     should then have 4 more columns for the parallel     version, but with 1, 2, 3, and 4 processes in the Pool."""     pass def time() :     """This function should generate a table of runtimes     using timeit.  Use the same columns and values of     n as in the generate_table() function.  When you use timeit     for this, pass number=1 (because the high n values will be slow)."""     pass

Follow the instructions in the screenshots provided carefully to implement the code below in python 3.10 or later, please.

def pi_monte_carlo(n) :
    """Computes and returns an estimation of pi
    using Monte Carlo simulation.

    Keyword arguments:
    n - The number of samples.
    """
    pass

def pi_parallel_monte_carlo(n, p=4) :
    """Computes and returns an estimation of pi
    using a parallel Monte Carlo simulation.

    Keyword arguments:
    n - The total number of samples.
    p - The number of processes to use.
    """
    # You can distribute the work across p
    # processes by having each process
    # call the sequential version, where
    # those calls divide the n samples across
    # the p calls.
    # Once those calls return, simply average
    # the p partial results and return that average.
    pass

def generate_table() :
    """This function should generate and print a table
    of results to demonstrate that both versions
    compute increasingly accurate estimations of pi
    as n is increased.  It should use the following
    values of n = {12, 24, 48, ..., 12582912}. That is,
    the first value of n is 12, and then each subsequent
    n is 2 times the previous.  The reason for starting at 12
    is so that n is always divisible by 1, 2, 3, and 4.
    The first
    column should be n, the second column should
    be the result of calling piMonteCarlo(n), and you
    should then have 4 more columns for the parallel
    version, but with 1, 2, 3, and 4 processes in the Pool."""
    pass

def time() :
    """This function should generate a table of runtimes
    using timeit.  Use the same columns and values of
    n as in the generate_table() function.  When you use timeit
    for this, pass number=1 (because the high n values will be slow)."""
    pass