class_docstr: Hierarchical latent variable model based on a diffusion model.
  
  Args:
      model: The model to use for the denoising (should be in eval mode).
      diffusion: The diffusion to sample from.
      use_log_variance: Whether to use the (clipped) log variance.
      clip_denoised: If True, clip the denoised signal.
      device: The device to use.
          Defaults to the device of the first model parameter.
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docstr: Outputs the mean and standard deviation of the Gaussian
  posterior q(z_{step}|data).
  
  Args:
      data: The input data array.
  Returns:
      The parameters (mean and stdd) of the Gaussian posterior.
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code: def infer_fn(self, data: np.ndarray) -> GaussianParams:
      """
      Outputs the mean and standard deviation of the Gaussian
      posterior q(z_{step}|data).
  
      Args:
          data: The input data array.
      Returns:
          The parameters (mean and stdd) of the Gaussian posterior.
      """
      t = torch.tensor([self.steps - 1], dtype=torch.long).expand(
          data.shape[0]
      )
      x_start = torch.from_numpy(data).to(dtype=torch.float)
  
      mean, variance, log_variance = self.diffusion.q_mean_variance(
          x_start,
          t=t,
      )
      return self._gaussian_params(mean, variance, log_variance)
  
imports: from abc import ABC, abstractmethod
  from typing import List, NamedTuple, Optional, Tuple, Union, overload
  import craystack
  import numpy
  import torch
  from craystack import codecs
  from craystack.bb_ans import BBANS
  from improved_diffusion.gaussian_diffusion import GaussianDiffusion
label_desc: Use image node_id 3 for calling the DiffusionModel obj's underlying member method code with example usage: obj.infer_fn(data) and returns: self