docstr: Extracts the embedding of the model.
  
  This is used in `CategoricalEmbeddingTransformer`
code: def extract_embedding(self):
      """Extracts the embedding of the model.
  
      This is used in `CategoricalEmbeddingTransformer`
      """
      if self.hparams.categorical_dim > 0:
          if not isinstance(self.embedding_layer, PreEncoded1dLayer):
              return self.embedding_layer.cat_embedding_layers
          else:
              raise ValueError(
                  "Cannot extract embedding for PreEncoded1dLayer. Please use a different embedding layer."
              )
      else:
          raise ValueError(
              "Model has been trained with no categorical feature and therefore can't be used"
              " as a Categorical Encoder"
          )
  
imports: import importlib
  import warnings
  from abc import ABCMeta, abstractmethod
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from omegaconf import DictConfig, OmegaConf
  from pandas import DataFrame
  from torch import Tensor
  from torch.optim import Optimizer
  from pytorch_tabular.models.common.heads import blocks
  from pytorch_tabular.models.common.layers import PreEncoded1dLayer
  from pytorch_tabular.utils import get_logger, reset_all_weights
label_desc: Use image node_id 21 for calling the BaseModel obj's underlying member method code with example usage: obj.extract_embedding() and returns: self