How to use the gpytorch.means.ConstantMean function in gpytorch

def __init__(self, inducing_points):
                variational_distribution = distribution_cls(num_inducing, batch_shape=batch_shape)
                variational_strategy = strategy_cls(
                    self, inducing_points, variational_distribution, learn_inducing_locations=True
                )
                super().__init__(variational_strategy)
                if constant_mean:
                    self.mean_module = gpytorch.means.ConstantMean()
                    self.mean_module.initialize(constant=1.0)
                else:
                    self.mean_module = gpytorch.means.ZeroMean()
                self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())

def __init__(self, train_inputs, train_targets, likelihood):
        super(ExactGPModel, self).__init__(train_inputs, train_targets, likelihood)
        self.mean_module = ConstantMean(prior=SmoothedBoxPrior(-1, 1))
        self.covar_module = ScaleKernel(
            RBFKernel(lengthscale_prior=SmoothedBoxPrior(math.exp(-3), math.exp(3), sigma=0.1))
        )

def __init__(self, train_x):
        variational_distribution = CholeskyVariationalDistribution(train_x.size(0))
        variational_strategy = VariationalStrategy(self, train_x, variational_distribution)
        super(GPClassificationModel, self).__init__(variational_strategy)
        self.mean_module = gpytorch.means.ConstantMean()
        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())

def __init__(self, train_inputs, train_targets, likelihood):
        super(ExactGPModel, self).__init__(train_inputs, train_targets, likelihood)
        self.mean_module = ConstantMean(prior=SmoothedBoxPrior(-1, 1))
        self.covar_module = ScaleKernel(RBFKernel(lengthscale_prior=SmoothedBoxPrior(exp(-3), exp(3), sigma=0.1)))

def test_ModelListGP(self):
        for dtype, use_octf in itertools.product(
            (torch.float, torch.double), (False, True)
        ):
            tkwargs = {"device": self.device, "dtype": dtype}
            model = _get_model(n=10, use_octf=use_octf, **tkwargs)
            self.assertIsInstance(model, ModelListGP)
            self.assertIsInstance(model.likelihood, LikelihoodList)
            for m in model.models:
                self.assertIsInstance(m.mean_module, ConstantMean)
                self.assertIsInstance(m.covar_module, ScaleKernel)
                matern_kernel = m.covar_module.base_kernel
                self.assertIsInstance(matern_kernel, MaternKernel)
                self.assertIsInstance(matern_kernel.lengthscale_prior, GammaPrior)
                if use_octf:
                    self.assertIsInstance(m.outcome_transform, Standardize)

            # test constructing likelihood wrapper
            mll = SumMarginalLogLikelihood(model.likelihood, model)
            for mll_ in mll.mlls:
                self.assertIsInstance(mll_, ExactMarginalLogLikelihood)

            # test model fitting (sequential)
            with warnings.catch_warnings():
                warnings.filterwarnings("ignore", category=OptimizationWarning)
                mll = fit_gpytorch_model(mll, options={"maxiter": 1}, max_retries=1)

def __init__(self, train_x, train_y, likelihood):
        super(MultitaskGPModel, self).__init__(train_x, train_y, likelihood)
        self.mean_module = MultitaskMean(ConstantMean(), num_tasks=2)
        self.base_kernel_list = [RBFKernel()]
        self.covar_module = LCMKernel(self.base_kernel_list, num_tasks=2, rank=1)

def __init__(self, inducing_points):
        variational_distribution = CholeskyVariationalDistribution(inducing_points.size(-1))
        variational_strategy = VariationalStrategy(
            self, inducing_points, variational_distribution, learn_inducing_locations=True
        )
        super(SVGPRegressionModel, self).__init__(variational_strategy)
        self.mean_module = gpytorch.means.ConstantMean()
        self.covar_module = gpytorch.kernels.ScaleKernel(
            gpytorch.kernels.RBFKernel(lengthscale_prior=gpytorch.priors.SmoothedBoxPrior(0.001, 1.0, sigma=0.1))
        )

def __init__(self, train_x, train_y, likelihood, kernel='linear'):
        super(ExactGPLayer, self).__init__(train_x, train_y, likelihood)
        self.mean_module  = gpytorch.means.ConstantMean()

        ## RBF kernel
        if(kernel=='rbf' or kernel=='RBF'):
            self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
        ## Spectral kernel
        elif(kernel=='spectral'):
            self.covar_module = gpytorch.kernels.SpectralMixtureKernel(num_mixtures=4, ard_num_dims=2916)
        else:
            raise ValueError("[ERROR] the kernel '" + str(kernel) + "' is not supported for regression, use 'rbf' or 'spectral'.")

else:
            raise ValueError(f"Unsupported shape {train_X.shape} for train_X.")

        # Set up noise model
        train_y_log_var = (
            2 * train_Y_se.log() + phi_func(train_phi.view_as(train_Y_se)).log()
        )
        noise_model = SingleTaskGP(train_X=train_x, train_Y=train_y_log_var)
        noise_covar = FidelityAwareHeteroskedasticNoise(
            noise_model=noise_model, x_idxr=x_idxr, phi_idxr=phi_idxr, phi_func=phi_func
        )
        likelihood = _GaussianLikelihoodBase(noise_covar)
        super().__init__(
            train_inputs=train_X, train_targets=train_Y, likelihood=likelihood
        )
        self.mean_module = ConstantMean(batch_size=batch_size)
        self.covar_module = ScaleKernel(
            base_kernel=MaternKernel(
                nu=2.5,
                ard_num_dims=ard_num_dims,
                lengthscale_prior=GammaPrior(3.0, 6.0),
                param_transform=softplus,
            ),
            batch_size=batch_size,
            param_transform=softplus,
            outputscale_prior=GammaPrior(2.0, 0.15),
        )