code: def forward(
      self,
      *params: Any,
      batch_shape: Optional[torch.Size] = None,
      shape: Optional[torch.Size] = None,
      noise: Optional[Tensor] = None,
  ) -> DiagLinearOperator:
      if noise is not None:
          return DiagLinearOperator(noise)
      training = self.noise_model.training  # keep track of mode
      try:
          self.noise_model.eval()  # we want the posterior prediction of the noise model
          with settings.detach_test_caches(False), settings.debug(
              False
          ):
              if len(params) == 1 and not torch.is_tensor(params[0]):
                  output = self.noise_model(*params[0])
              else:
                  output = self.noise_model(*params)
      finally:
          self.noise_model.train(training)
      if not isinstance(output, MultivariateNormal):
          raise NotImplementedError(
              "Currently only noise models that return a MultivariateNormal are supported"
          )
      # note: this also works with MultitaskMultivariateNormal, where this
      # will return a batched DiagLinearOperators of size n x num_tasks x num_tasks
      noise_diag = (
          output.mean
          if self._noise_indices is None
          else output.mean[..., self._noise_indices]
      )
      return DiagLinearOperator(
          self._noise_constraint.transform(noise_diag)
      )
  
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 HeteroskedasticNoise obj's underlying member method code with example usage: obj.forward() and returns: DiagLinearOperator, DiagLinearOperator