How to use the gpytorch.kernels function in gpytorch

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_x, train_y, likelihood):
        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)
        self.mean_module = gpytorch.means.ConstantMean()
        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())

def __init__(self):
                variational_distribution = distribution_cls(num_inducing ** 2, batch_shape=batch_shape)
                variational_strategy = strategy_cls(self, num_inducing, [(-3, 3), (-3, 3)], variational_distribution)
                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 sq_dist_func(x1, x2):
    dist_module = gpytorch.kernels.kernel.Distance()
    return dist_module._sq_dist(x1, x2, postprocess=torch.tensor(False))

def __init__(self, inducing_points, learn_locs=True):
        variational_distribution = CholeskyVariationalDistribution(inducing_points.size(-1))
        variational_strategy = WhitenedVariationalStrategy(
            self, inducing_points, variational_distribution, learn_inducing_locations=learn_locs
        )
        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):
        super().__init__(train_x, train_y, likelihood)
        self.mean_module = gpytorch.means.ConstantMean()
        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())

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_x, train_y, likelihood):
        super().__init__(train_x, train_y, likelihood)
        self.mean_module = gpytorch.means.ConstantMean()
        covar_a = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
        covar_b = gpytorch.kernels.ScaleKernel(gpytorch.kernels.MaternKernel(nu=0.5))
        self.covar_module = covar_a + covar_b

def __init__(self, train_x, train_y, num_functions=2):
            # Define all the variational stuff
            inducing_points = torch.linspace(0, 1, 64).unsqueeze(-1)
            variational_distribution = gpytorch.variational.CholeskyVariationalDistribution(
                num_inducing_points=inducing_points.size(-2), batch_shape=torch.Size([num_functions])
            )
            variational_strategy = gpytorch.variational.MultitaskVariationalStrategy(
                gpytorch.variational.VariationalStrategy(self, inducing_points, variational_distribution),
                num_tasks=num_functions,
            )
            super().__init__(variational_strategy)

            # Mean, covar
            self.mean_module = gpytorch.means.ZeroMean()
            self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())

            # Values to store
            self.name_prefix = "llcmgp"
            self.num_data, self.num_tasks = train_y.shape
            self.num_functions = num_functions

            # Define likelihood stuff
            self.register_parameter(
                "variational_logits", torch.nn.Parameter(torch.randn(self.num_tasks, self.num_functions))
            )
            self.register_parameter("raw_noise", torch.nn.Parameter(torch.tensor(0.0)))

def __init__(self, train_x, train_y, likelihood):
                super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)
                self.mean_module = gpytorch.means.ConstantMeanGrad()
                self.base_kernel = gpytorch.kernels.RBFKernelGrad(ard_num_dims=num_params)
                self.covar_module = gpytorch.kernels.ScaleKernel(self.base_kernel)