code: def sample(
      self,
      x: Dict,
      n_samples: Optional[int] = None,
      ret_model_output=False,
  ):
      ret_value = self.forward(x)
      samples = self.head.generate_samples(
          ret_value["pi"],
          ret_value["sigma"],
          ret_value["mu"],
          n_samples,
      )
      if ret_model_output:
          return samples, ret_value
      else:
          return samples
  
imports: from typing import Dict, Optional, Union
  import torch
  import torch.nn
  from omegaconf import DictConfig, OmegaConf
  from torch import Tensor
  from pytorch_tabular import models
  from pytorch_tabular.config.config import ModelConfig
  from pytorch_tabular.models.common.heads import blocks
  from pytorch_tabular.models.tab_transformer.tab_transformer import TabTransformerBackbone
  from pytorch_tabular.tabular_model import getattr_nested
  from pytorch_tabular.utils import get_logger
  from ..base_model import BaseModel, safe_merge_config
label_desc: Use image node_id 11 for calling the MDNModel obj's underlying member method code with example usage: obj.sample(x, n_samples, ret_model_output) and returns: samples,  ret_value, samples