def _plot_samples(self, canvas, helper_data, helper_prediction, projection,
              label, **kwargs):
    _, free_dims, Xgrid, x, y, _, _, resolution = helper_data
    samples = helper_prediction[2]

    if 2 <= len(free_dims):
        if len(free_dims)==1:
            # 1D plotting:
            update_not_existing_kwargs(kwargs, pl().defaults.samples_1d)  # @UndefinedVariable
            plots = [pl().plot(canvas, Xgrid[:, free_dims], samples[:, s], label=label if s==0 else None, **kwargs) for s in range(samples.shape[-1])]
        elif len(free_dims)==2 and projection=='3d':
            update_not_existing_kwargs(kwargs, pl().defaults.samples_3d)  # @UndefinedVariable
            plots = [pl().surface(canvas, x, y, samples[:, s].reshape(resolution, resolution), **kwargs) for s in range(samples.shape[-1])]
        else:
            pass # Nothing to plot!
        return dict(gpmean=plots)
    else:
        raise RuntimeError('Cannot plot mean in more then 1 input dimensions')
dataset/ETHPy150Open SheffieldML/GPy/GPy/plotting/gpy_plot/gp_plots.py/_plot_samples/swapped operands of `<=`