docstr: Initializes the Transformer and extracts the neural embeddings.
  
  Args:
      tabular_model (TabularModel): The trained TabularModel object
docstr_tok: ["Initializes","the","Transformer","and","extracts","the","neural","embeddings",".","Args",":","tabular_model","(","TabularModel",")",":","The","trained","TabularModel","object"]
code: def __init__(self, tabular_model):
      """Initializes the Transformer and extracts the neural embeddings.
  
      Args:
          tabular_model (TabularModel): The trained TabularModel object
      """
      self._categorical_encoder = (
          tabular_model.datamodule.categorical_encoder
      )
      self.cols = tabular_model.model.hparams.categorical_cols
      # dict {str: np.ndarray} column name --> mapping from category (index of df) to value (column of df)
      self._mapping = {}
  
      self._extract_embedding(tabular_model.model)
  
imports: from pandas import DataFrame, Series, unique
  import numpy
  from rich.progress import track
  from sklearn.base import BaseEstimator, TransformerMixin
  from pytorch_tabular.utils import get_logger
label_desc: Use image node_id 1 to create a new CategoricalEmbeddingTransformer object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = CategoricalEmbeddingTransformer(tabular_model)