docstr: Initializes the Transformer and extracts the neural features.
  
  Args:
      tabular_model (TabularModel): The trained TabularModel object
      extract_keys (list, optional): The keys of the features to extract. Defaults to ["backbone_features"].
      drop_original (bool, optional): Whether to drop the original columns. Defaults to True.
docstr_tok: ["Initializes","the","Transformer","and","extracts","the","neural","features",".","Args",":","tabular_model","(","TabularModel",")",":","The","trained","TabularModel","object","extract_keys","(","list",",","optional",")",":","The","keys","of","the","features","to","extract",".","Defaults","to","[","``","backbone_features","''","]",".","drop_original","(","bool",",","optional",")",":","Whether","to","drop","the","original","columns",".","Defaults","to","True","."]
code: def __init__(
      self,
      tabular_model,
      extract_keys=["backbone_features"],
      drop_original=True,
  ):
      """Initializes the Transformer and extracts the neural features.
  
      Args:
          tabular_model (TabularModel): The trained TabularModel object
          extract_keys (list, optional): The keys of the features to extract. Defaults to ["backbone_features"].
          drop_original (bool, optional): Whether to drop the original columns. Defaults to True.
      """
      assert not (
          isinstance(tabular_model.model, NODEModel)
          or isinstance(tabular_model.model, TabNetModel)
          or isinstance(tabular_model.model, MDNModel)
      ), "FeatureExtractor doesn't work for Mixture Density Networks, NODE Model, & Tabnet Model"
      self.tabular_model = tabular_model
      self.extract_keys = extract_keys
      self.drop_original = drop_original
  
file_path: pytorch_tabular/src/pytorch_tabular/feature_extractor.py
imports: from collections import defaultdict
  import pandas
  from rich.progress import track
  from sklearn.base import BaseEstimator, TransformerMixin
  from pytorch_tabular.models import NODEModel, TabNetModel
  from pytorch_tabular.models.mixture_density import MDNModel
  import torch
label_desc: Use image node_id 1 to create a new DeepFeatureExtractor object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = DeepFeatureExtractor(tabular_model, extract_keys, drop_original)