class_docstr: A (non-zero) constant prior mean function and its first and second derivatives, i.e.:
  
  .. math::
  
      \mu(\mathbf x) &= C \\
      \nabla \mu(\mathbf x) &= \mathbf 0 \\
      \nabla^2 \mu(\mathbf x) &= \mathbf 0
  
  where :math:`C` is a learned constant.
  
  :param prior: Prior for constant parameter :math:`C`.
  :type prior: ~gpytorch.priors.Prior, optional
  :param batch_shape: The batch shape of the learned constant(s) (default: []).
  :type batch_shape: torch.Size, optional
  
  :var torch.Tensor constant: :math:`C` parameter
code: def __init__(
      self,
      prior: Optional[Prior] = None,
      batch_shape: torch.Size = torch.Size(),
      **kwargs: Any,
  ):
      super(ConstantMeanGradGrad, self).__init__()
      self.batch_shape = batch_shape
      self.register_parameter(
          name="constant",
          parameter=torch.nn.Parameter(torch.zeros(*batch_shape, 1)),
      )
      if prior is not None:
          self.register_prior("mean_prior", prior, "constant")
  
imports: from typing import Any, Optional
  import torch
  from ..priors import Prior
  from .mean import Mean
label_desc: Use image node_id 1 to create a new ConstantMeanGradGrad object from inherited base classes: Mean with example: obj = ConstantMeanGradGrad(prior, batch_shape)