class_docstr: NaN handling policy for observations.
  
  * ``ignore``: Do not check for NaN values (the default).
  * ``mask``: Mask out NaN values during calculation. If an output is NaN in a single batch element, this output
    is masked for the complete batch. This strategy likely is a good choice if you have NaN values.
  * ``fill``: Fill in NaN values with a dummy value, perform computations and filter them later.
    Not supported by :class:`gpytorch.mlls.ExactMarginalLogLikelihood`.
    Does not support lazy covariance matrices during prediction.
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docstr: Fills a tensor's NaN values with a filling value.
  This function is used if observation_nan_policy is set to 'fill'.
  
  :param Tensor observations: The tensor to fill with values.
  :return: The filled in observations.
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code: def _fill_tensor(cls, observations) -> Tensor:
      """
      Fills a tensor's NaN values with a filling value.
      This function is used if observation_nan_policy is set to 'fill'.
  
      :param Tensor observations: The tensor to fill with values.
      :return: The filled in observations.
      """
      return torch.nan_to_num(observations, nan=cls._fill_value)
  
imports: import torch
  from linear_operator.settings import _linalg_dtype_cholesky, _linalg_dtype_symeig, cg_tolerance, cholesky_jitter, cholesky_max_tries, ciq_samples, deterministic_probes, fast_computations, linalg_dtypes, max_cg_iterations, max_cholesky_size, max_lanczos_quadrature_iterations, max_preconditioner_size, max_root_decomposition_size, min_preconditioning_size, minres_tolerance, num_contour_quadrature, num_trace_samples, preconditioner_tolerance, skip_logdet_forward, terminate_cg_by_size, tridiagonal_jitter, use_toeplitz, verbose_linalg
  from torch import Tensor
label_desc: Use image node_id 3 for calling the observation_nan_policy obj's underlying member method code with example usage: obj._fill_tensor(cls, observations) and returns: torch