def helper_for_plot_data(self, X, plot_limits, visible_dims, fixed_inputs, resolution):
<EOL>if fixed_inputs is None:<EOL><INDENT>fixed_inputs = []<EOL><DEDENT>fixed_dims = get_fixed_dims(fixed_inputs)<EOL>free_dims = get_free_dims(self, visible_dims, fixed_dims)<EOL>if len(free_dims) == <NUM_LIT:1>:<EOL><INDENT>resolution = resolution or <NUM_LIT:200><EOL>Xnew, xmin, xmax = x_frame1D(X[:,free_dims], plot_limits=plot_limits, resolution=resolution)<EOL>Xgrid = np.zeros((Xnew.shape[<NUM_LIT:0>],self.input_dim))<EOL>Xgrid[:,free_dims] = Xnew<EOL>for i,v in fixed_inputs:<EOL><INDENT>Xgrid[:,i] = v<EOL><DEDENT>x = Xgrid<EOL>y = None<EOL><DEDENT>elif len(free_dims) == <NUM_LIT:2>:<EOL><INDENT>resolution = resolution or <NUM_LIT><EOL>Xnew, x, y, xmin, xmax = x_frame2D(X[:,free_dims], plot_limits, resolution)<EOL>Xgrid = np.zeros((Xnew.shape[<NUM_LIT:0>], self.input_dim))<EOL>Xgrid[:,free_dims] = Xnew<EOL>for i,v in fixed_inputs:<EOL><INDENT>Xgrid[:,i] = v<EOL><DEDENT><DEDENT>else:<EOL><INDENT>raise TypeError("<STR_LIT>".format(free_dims, visible_dims, fixed_dims))<EOL><DEDENT>return fixed_dims, free_dims, Xgrid, x, y, xmin, xmax, resolution<EOL>
Figure out the data, free_dims and create an Xgrid for
the prediction.

This is only implemented for two dimensions for now!