class_docstr: Computes a covariance matrix based on the Linear kernel
  between inputs :math:`\mathbf{x_1}` and :math:`\mathbf{x_2}`:
  
  .. math::
      \begin{equation*}
          k_\text{Linear}(\mathbf{x_1}, \mathbf{x_2}) = v\mathbf{x_1}^\top
          \mathbf{x_2}.
      \end{equation*}
  
  where
  
  * :math:`v` is a variance parameter.
  
  
  .. note::
  
      To implement this efficiently, we use a
      :obj:`~linear_operator.operators.RootLinearOperator` during training
      and a :class:`~linear_operator.operators.MatmulLinearOperator` during
      test. These lazy tensors represent matrices of the form :math:`\mathbf
      K = \mathbf X \mathbf X^{\prime \top}`. This makes inference efficient
      because a matrix-vector product :math:`\mathbf K \mathbf v` can be
      computed as :math:`\mathbf K \mathbf v = \mathbf X( \mathbf X^{\prime
      \top} \mathbf v)`, where the base multiply :math:`\mathbf X \mathbf v`
      takes only :math:`\mathcal O(ND)` time and space.
  
  :param variance_prior: Prior over the variance parameter. (Default `None`.)
  :param variance_constraint: Constraint to place on variance parameter. (Default: `Positive`.)
  :param active_dims: List of data dimensions to operate on. `len(active_dims)` should equal `num_dimensions`.
code: def variance(self) -> Tensor:
      return self.raw_variance_constraint.transform(self.raw_variance)
  
file_path: gpytorch/gpytorch/kernels/linear_kernel.py
imports: import warnings
  from typing import Optional, Union
  import torch
  from linear_operator.operators import LinearOperator, MatmulLinearOperator, RootLinearOperator
  from torch import Tensor
  from ..constraints import Interval, Positive
  from ..priors import Prior
  from .kernel import Kernel
label_desc: Use image node_id 2 for calling the LinearKernel obj's underlying member method code with example usage: obj.variance() and returns: self