class_docstr: Computes a covariance matrix of the RBF kernel that models the covariance
  between the values and first and second (non-mixed) partial derivatives for inputs :math:`\mathbf{x_1}`
  and :math:`\mathbf{x_2}`.
  
  See :class:`gpytorch.kernels.Kernel` for descriptions of the lengthscale options.
  
  .. note::
  
      This kernel does not have an `outputscale` parameter. To add a scaling parameter,
      decorate this kernel with a :class:`gpytorch.kernels.ScaleKernel`.
  
  :param ard_num_dims: Set this if you want a separate lengthscale for each input
      dimension. It should be `d` if x1 is a `n x d` matrix. (Default: `None`.)
  :param batch_shape: Set this if you want a separate lengthscale for each batch of input
      data. It should be :math:`B_1 \times \ldots \times B_k` if :math:`\mathbf x1` is
      a :math:`B_1 \times \ldots \times B_k \times N \times D` tensor.
  :param active_dims: Set this if you want to compute the covariance of only
      a few input dimensions. The ints corresponds to the indices of the
      dimensions. (Default: `None`.)
  :param lengthscale_prior: Set this if you want to apply a prior to the
      lengthscale parameter. (Default: `None`)
  :param lengthscale_constraint: Set this if you want to apply a constraint
      to the lengthscale parameter. (Default: `Positive`.)
  :param eps: The minimum value that the lengthscale can take (prevents
      divide by zero errors). (Default: `1e-6`.)
  
  :ivar torch.Tensor lengthscale: The lengthscale parameter. Size/shape of parameter depends on the
      ard_num_dims and batch_shape arguments.
  
  Example:
      >>> x = torch.randn(10, 5)
      >>> # Non-batch: Simple option
      >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernelGradGrad())
      >>> covar = covar_module(x)  # Output: LinearOperator of size (110 x 110), where 110 = n * (2*d + 1)
      >>>
      >>> batch_x = torch.randn(2, 10, 5)
      >>> # Batch: Simple option
      >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernelGradGrad())
      >>> # Batch: different lengthscale for each batch
      >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernelGradGrad(batch_shape=torch.Size([2])))
      >>> covar = covar_module(x)  # Output: LinearOperator of size (2 x 110 x 110)
code: def num_outputs_per_input(self, x1, x2):
      return x1.size(-1) * 2 + 1
  
imports: import torch
  from linear_operator.operators import KroneckerProductLinearOperator
  from .rbf_kernel import postprocess_rbf, RBFKernel
label_desc: Use image node_id 2 for calling the RBFKernelGradGrad obj's underlying member method code with example usage: obj.num_outputs_per_input(x1, x2) and returns: unknown