code: def sample_elbo(
      self, input: torch.Tensor, target: torch.Tensor, num_samples: int
  ):
      # rlt.BanditRewardModelInput
  
      # we calculate the negative elbo, which will be our loss function
      # initialize tensors
      outputs = torch.zeros(num_samples, target.shape[0])
      log_priors = torch.zeros(num_samples)
      log_posts = torch.zeros(num_samples)
      log_likes = torch.zeros(num_samples)
      # make predictions and calculate prior, posterior, and likelihood for a given number of samples
      for i in range(num_samples):
          outputs[i] = self(input).reshape(-1)  # make predictions
          log_priors[i] = self.log_prior()  # get log prior
          log_posts[
              i
          ] = self.log_post()  # get log variational posterior
          log_likes[i] = (
              Normal(outputs[i], self.noise_tol)
              .log_prob(target.reshape(-1))
              .sum()
          )  # calculate the log likelihood
      # calculate monte carlo estimate of prior posterior and likelihood
      log_prior = log_priors.mean()
      log_post = log_posts.mean()
      log_like = log_likes.mean()
      # calculate the negative elbo (which is our loss function)
      loss = log_post - log_prior - log_like
      return loss
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.models.base import ModelBase
  from reagent.models.fully_connected_network import ACTIVATION_MAP, gaussian_fill_w_gain, SlateBatchNorm1d
  from torch.distributions import Normal
label_desc: Use image node_id 6 for calling the FullyConnectedProbabilisticNetwork obj's underlying member method code with example usage: obj.sample_elbo(input, target, num_samples) and returns: loss