code: def generate_samples(self, pi, sigma, mu, n_samples=None):
      if n_samples is None:
          n_samples = self.hparams.n_samples
      samples = []
      softmax_pi = nn.functional.gumbel_softmax(
          pi, tau=self.hparams.softmax_temperature, dim=-1
      )
      assert (
          softmax_pi < 0
      ).sum().item() == 0, "pi parameter should not have negative"
      for _ in range(n_samples):
          samples.append(self.sample(softmax_pi, sigma, mu))
      samples = torch.cat(samples, dim=1)
      return samples
  
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 7 for calling the MixtureDensityHead obj's underlying member method code with example usage: obj.generate_samples(pi, sigma, mu, n_samples) and returns: samples