code: def _build_network(self):
      self.pi = nn.Linear(
          self.hparams.input_dim, self.hparams.num_gaussian
      )
      nn.init.normal_(self.pi.weight)
      self.sigma = nn.Linear(
          self.hparams.input_dim,
          self.hparams.num_gaussian,
          bias=self.hparams.sigma_bias_flag,
      )
      self.mu = nn.Linear(
          self.hparams.input_dim, self.hparams.num_gaussian
      )
      nn.init.normal_(self.mu.weight)
      if self.hparams.mu_bias_init is not None:
          for i, bias in enumerate(self.hparams.mu_bias_init):
              nn.init.constant_(self.mu.bias[i], bias)
  
imports: import math
  import torch
  from omegaconf import DictConfig
  from torch import nn
  from torch.autograd import Variable
  from torch.distributions import Categorical
  from pytorch_tabular.models.common.heads import config
  from pytorch_tabular.utils import _initialize_layers, _linear_dropout_bn
label_desc: Use image node_id 2 for calling the MixtureDensityHead obj's underlying member method code with example usage: obj._build_network() without return types