docstr: Encode given columns of X based on the learned embedding.
  
  Args:
      X (DataFrame): DataFrame of features, shape (n_samples, n_features). Must contain columns to encode.
      y ([type], optional): Only for compatibility. Not used. Defaults to None.
  
  Returns:
      DataFrame: The encoded dataframe
docstr_tok: ["Encode","given","columns","of","X","based","on","the","learned","embedding",".","Args",":","X","(","DataFrame",")",":","DataFrame","of","features",",","shape","(","n_samples",",","n_features",")",".","Must","contain","columns","to","encode",".","y","(","[","type","]",",","optional",")",":","Only","for","compatibility",".","Not","used",".","Defaults","to","None",".","Returns",":","DataFrame",":","The","encoded","dataframe"]
code: def fit_transform(self, X: DataFrame, y=None) -> DataFrame:
      """Encode given columns of X based on the learned embedding.
  
      Args:
          X (DataFrame): DataFrame of features, shape (n_samples, n_features). Must contain columns to encode.
          y ([type], optional): Only for compatibility. Not used. Defaults to None.
  
      Returns:
          DataFrame: The encoded dataframe
      """
      self.fit(X, y)
      return self.transform(X)
  
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 5 for calling the CategoricalEmbeddingTransformer obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self