
for i in range(n_dimensions):
    for j in range(i + 1, n_dimensions):
        # Create an objective function for the current pair of dimensions
        def partial_objective_function(x):
            partial_x = [0] * n_dimensions
            partial_x[i] = x[0]
            partial_x[j] = x[1]
            return objective_function(partial_x)

        # Run the optimization for the current pair of dimensions
        partial_search_space = [search_space[i], search_space[j]]
        result = gp_minimize(func=partial_objective_function, dimensions=partial_search_space, n_calls=50, random_state=0)

        # Plot the Gaussian process for the current pair of dimensions
        plot_gaussian_process(result, ax=axes[i, j])
        axes[i, j].set_xlabel(f'Dimension {i}')
        axes[i, j].set_ylabel(f'Dimension {j}')

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