code: def get_params(self, y_dict, design, target_labels):
      y = torch.cat(list(y_dict.values()), dim=-1)
  
      coefficient_labels = [
          label for label in target_labels if label != self.tau_label
      ]
      mu, scale_tril = self.linear_model_formula(
          y, design, coefficient_labels
      )
      mu_vec = torch.cat(list(mu.values()), dim=-1)
  
      yty = rvv(y, y)
      ytxmu = rvv(y, rmv(design, mu_vec))
      beta = self.b0 + 0.5 * (yty - ytxmu)
  
      return mu, scale_tril, self.alpha, beta
  
imports: from contextlib import ExitStack
  import torch
  from torch import nn
  import pyro
  import pyro.distributions
  from pyro import poutine
  from pyro.contrib.util import iter_plates_to_shape, lexpand, rmv, rtril, rvv, tensor_to_dict
  from pyro.ops.linalg import rinverse
label_desc: Use image node_id 2 for calling the NormalInverseGammaGuide obj's underlying member method code with example usage: obj.get_params(y_dict, design, target_labels) and returns: mu,  scale_tril,  self,  beta