code: def get_robust_regression(device: torch.device) -> GetterReturnType:
      N = 10
      K = 10
  
      # X.shape: (N, K + 1), Y.shape: (N, 1)
      X = torch.rand(N, K + 1, device=device)
      Y = torch.rand(N, 1, device=device)
  
      # Predefined nu_alpha and nu_beta, nu_alpha.shape: (1, 1), nu_beta.shape: (1, 1)
      nu_alpha = torch.rand(1, 1, device=device)
      nu_beta = torch.rand(1, 1, device=device)
      nu = dist.Gamma(nu_alpha, nu_beta)
  
      # Predefined sigma_rate: sigma_rate.shape: (N, 1)
      sigma_rate = torch.rand(N, 1, device=device)
      sigma = dist.Exponential(sigma_rate)
  
      # Predefined beta_mean and beta_sigma: beta_mean.shape: (K + 1, 1), beta_sigma.shape: (K + 1, 1)
      beta_mean = torch.rand(K + 1, 1, device=device)
      beta_sigma = torch.rand(K + 1, 1, device=device)
      beta = dist.Normal(beta_mean, beta_sigma)
  
      nu_value = nu.sample()
      nu_value.requires_grad_(True)
  
      sigma_value = sigma.sample()
      sigma_unconstrained_value = sigma_value.log()
      sigma_unconstrained_value.requires_grad_(True)
  
      beta_value = beta.sample()
      beta_value.requires_grad_(True)
  
      def forward(
          nu_value: Tensor,
          sigma_unconstrained_value: Tensor,
          beta_value: Tensor,
      ) -> Tensor:
          sigma_constrained_value = sigma_unconstrained_value.exp()
          mu = X.mm(beta_value)
  
          # For this model, we need to compute the following three scores:
          # We need to compute the first and second gradient of this score with respect
          # to nu_value.
          nu_score = dist.StudentT(
              nu_value, mu, sigma_constrained_value
          ).log_prob(Y).sum() + nu.log_prob(nu_value)
  
          # We need to compute the first and second gradient of this score with respect
          # to sigma_unconstrained_value.
          sigma_score = (
              dist.StudentT(nu_value, mu, sigma_constrained_value)
              .log_prob(Y)
              .sum()
              + sigma.log_prob(sigma_constrained_value)
              + sigma_unconstrained_value
          )
  
          # We need to compute the first and second gradient of this score with respect
          # to beta_value.
          beta_score = dist.StudentT(
              nu_value, mu, sigma_constrained_value
          ).log_prob(Y).sum() + beta.log_prob(beta_value)
  
          return nu_score.sum() + sigma_score.sum() + beta_score.sum()
  
      return forward, (
          nu_value.to(device),
          sigma_unconstrained_value.to(device),
          beta_value.to(device),
      )
  
imports: import torch
  import torch.distributions
  from torch import Tensor
  from utils import GetterReturnType
label_desc: Use image node_id 2 for calling a global function with example usage: get_robust_regression(device) and returns: forward,  nu_value,  sigma_unconstrained_value,  beta_value, unknown