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 forward(self, input):
      batch_shape = torch.broadcast_shapes(
          self.batch_shape, input.shape[:-2]
      )
      mean = (
          self.constant.unsqueeze(-1)
          .expand(*batch_shape, input.size(-2), 2 * input.size(-1) + 1)
          .contiguous()
      )
      mean[..., 1:] = 0
      return mean
  
imports: from typing import Any, Optional
  import torch
  from ..priors import Prior
  from .mean import Mean
label_desc: Use image node_id 2 for calling the ConstantMeanGradGrad obj's underlying member method code with example usage: obj.forward(input) and returns: mean