class_docstr: Implements the Matern kernel using KeOps as a driver for kernel matrix multiplies.
  
  This class can be used as a drop in replacement for :class:`gpytorch.kernels.MaternKernel` in most cases,
  and supports the same arguments.
  
  :param nu: (Default: 2.5) The smoothness parameter.
  :type nu: float (0.5, 1.5, or 2.5)
  :param ard_num_dims: (Default: `None`) Set this if you want a separate lengthscale for each
      input dimension. It should be `d` if x1 is a `... x n x d` matrix.
  :type ard_num_dims: int, optional
  :param batch_shape: (Default: `None`) Set this if you want a separate lengthscale for each
       batch of input data. It should be `torch.Size([b1, b2])` for a `b1 x b2 x n x m` kernel output.
  :type batch_shape: torch.Size, optional
  :param active_dims: (Default: `None`) Set this if you want to
      compute the covariance of only a few input dimensions. The ints
      corresponds to the indices of the dimensions.
  :type active_dims: Tuple(int)
  :param lengthscale_prior: (Default: `None`)
      Set this if you want to apply a prior to the lengthscale parameter.
  :type lengthscale_prior: ~gpytorch.priors.Prior, optional
  :param lengthscale_constraint: (Default: `Positive`) Set this if you want
      to apply a constraint to the lengthscale parameter.
  :type lengthscale_constraint: ~gpytorch.constraints.Interval, optional
  :param eps: (Default: 1e-6) The minimum value that the lengthscale can take (prevents divide by zero errors).
  :type eps: float, optional
code: def __init__(self, nu=2.5, **kwargs):
      if nu not in {0.5, 1.5, 2.5}:
          raise RuntimeError("nu expected to be 0.5, 1.5, or 2.5")
      super().__init__(**kwargs)
      self.nu = nu
  
imports: import math
  from linear_operator.operators import KernelLinearOperator
  from .keops_kernel import _lazify_and_expand_inputs, KeOpsKernel
label_desc: Use image node_id 1 to create a new MaternKernel object from inherited base classes: KeOpsKernel with example: obj = MaternKernel(nu)