class_docstr: Computes a covariance matrix based on the RBF (squared exponential) kernel
  between inputs :math:`\mathbf{x_1}` and :math:`\mathbf{x_2}`:
  
  .. math::
  
     \begin{equation*}
        k_{\text{RBF}}(\mathbf{x_1}, \mathbf{x_2}) = \exp \left( -\frac{1}{2}
        (\mathbf{x_1} - \mathbf{x_2})^\top \Theta^{-2} (\mathbf{x_1} - \mathbf{x_2}) \right)
     \end{equation*}
  
  where :math:`\Theta` is a lengthscale parameter.
  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.RBFKernel())
      >>> # Non-batch: ARD (different lengthscale for each input dimension)
      >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel(ard_num_dims=5))
      >>> covar = covar_module(x)  # Output: LinearOperator of size (10 x 10)
      >>>
      >>> batch_x = torch.randn(2, 10, 5)
      >>> # Batch: Simple option
      >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
      >>> # Batch: different lengthscale for each batch
      >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel(batch_shape=torch.Size([2])))
      >>> covar = covar_module(x)  # Output: LinearOperator of size (2 x 10 x 10)
code: def forward(self, x1, x2, diag=False, **params):
      if (
          x1.requires_grad
          or x2.requires_grad
          or (self.ard_num_dims is not None and self.ard_num_dims > 1)
          or diag
          or params.get("last_dim_is_batch", False)
          or trace_mode.on()
      ):
          x1_ = x1.div(self.lengthscale)
          x2_ = x2.div(self.lengthscale)
          return postprocess_rbf(
              self.covar_dist(
                  x1_, x2_, square_dist=True, diag=diag, **params
              )
          )
      return RBFCovariance.apply(
          x1,
          x2,
          self.lengthscale,
          lambda x1, x2: self.covar_dist(
              x1, x2, square_dist=True, diag=False, **params
          ),
      )
  
imports: from ..functions import RBFCovariance
  from ..settings import trace_mode
  from .kernel import Kernel
label_desc: Use image node_id 1 for calling the RBFKernel obj's underlying member method code with example usage: obj.forward(x1, x2, diag) and returns: RBFCovariance, postprocess_rbf