def __init__(self, input_dim, num_data, variance=<NUM_LIT:1.>, active_dims=None, name='<STR_LIT>'):
super(Static, self).__init__(input_dim, active_dims, name)<EOL>self.variance = Param('<STR_LIT>', np.ones(num_data) * variance, Logexp())<EOL>self.link_parameters(self.variance)<EOL>
A heteroscedastic White kernel (nugget/noise).
It defines one variance (nugget) per input sample.

Prediction excludes any noise learnt by this Kernel, so be careful using this kernel.

You can plot the errors learnt by this kernel by something similar as:
plt.errorbar(m.X, m.Y, yerr=2*np.sqrt(m.kern.white.variance))