import numpy as np

# Define the payoff matrix
payoff_matrix = np.array([[[0, 10], [0, 8], [0, 6]],
                          [[8, -10], [-2, 8], [-2, 6]],
                          [[6, -10], [6, -12], [-4, 6]]])

# Find the maximum payoffs for the attacker and the defender
max_payoffs_attacker = np.max(payoff_matrix, axis=1)
max_payoffs_defender = np.max(payoff_matrix, axis=2)

# Find the indices of the maximum payoffs for the attacker and the defender
max_indices_attacker = np.argmax(payoff_matrix, axis=1)
max_indices_defender = np.argmax(payoff_matrix, axis=2)

# Find the strategies that correspond to the Nash equilibrium
nash_equilibria = []
for i in range(payoff_matrix.shape[1]):
    for j in range(payoff_matrix.shape[2]):
        if (max_indices_attacker[i][j] == j) and (max_indices_defender[i][j] == i):
            nash_equilibria.append((i, j))

# Print the Nash equilibrium(s)
if len(nash_equilibria) == 0:
    print("There is no Nash equilibrium.")
else:
    print("The Nash equilibrium(s) is/are:")
    for s in nash_equilibria:
        print("Attacker chooses strategy", s[0], "and defender chooses strategy", s[1])