l : float, default=1.0
    Parameter controlling the noise level (variance)

noise_level_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5)
    The lower and upper bound on 'noise_level'.
    If set to "fixed", 'noise_level' cannot be changed during
    hyperparameter tuning.

Examples
--------
>>> from sklearn.datasets import make_friedman2
>>> from sklearn.gaussian_process import GaussianProcessRegressor
>>> from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel
>>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
>>> kernel = DotProduct() + WhiteKernel(noise_level=0.5)
>>> gpr = GaussianProcessRegressor(kernel=kernel,
...         random_state=0).fit(X, y)
>>> gpr.score(X, y)
0.3680...
>>> gpr.predict(X[:2,:], return_std=True)
(array([653.0..., 592.1... ]), array([316.6..., 316.6...]))
c