code: def forward(self, x):
      pi = self.pi(x)
      sigma = self.sigma(x)
      # Applying modified ELU activation
      sigma = nn.ELU()(sigma) + 1 + 1e-15
      mu = self.mu(x)
      return pi, sigma, mu
  
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 3 for calling the MixtureDensityHead obj's underlying member method code with example usage: obj.forward(x) and returns: pi,  sigma,  mu