code: def forward(
      self,
      *params: Any,
      shape: Optional[torch.Size] = None,
      noise: Optional[Tensor] = None,
      **kwargs: Any,
  ) -> DiagLinearOperator:
      if shape is None:
          p = params[0] if torch.is_tensor(params[0]) else params[0][0]
          shape = p.shape if len(p.shape) == 1 else p.shape[:-1]
  
      if noise is not None:
          return DiagLinearOperator(noise)
      elif shape[-1] == self.noise.shape[-1]:
          return DiagLinearOperator(self.noise)
      else:
          return ZeroLinearOperator()
  
imports: import warnings
  from typing import Any, Optional, Union
  import torch
  from linear_operator.operators import ConstantDiagLinearOperator, DiagLinearOperator, LinearOperator, ZeroLinearOperator
  from torch import Tensor
  from torch.nn import Parameter
  from ..None import settings
  from ..constraints import GreaterThan
  from ..distributions import MultivariateNormal
  from ..module import Module
  from ..utils.warnings import NumericalWarning
label_desc: Use image node_id 2 for calling the FixedGaussianNoise obj's underlying member method code with example usage: obj.forward() and returns: DiagLinearOperator, DiagLinearOperator, ZeroLinearOperator