docstr: Fits the DragonNet model and then predicts.
  
  Args:
      X (np.matrix or np.array or pd.Dataframe): a feature matrix
      treatment (np.array or pd.Series): a treatment vector
      y (np.array or pd.Series): an outcome vector
      return_components (bool, optional): whether to return
  Returns:
      (np.array): predictions based on return_components flag
          if return_components=False (default), each row is treatment effect
          if return_components=True, each row is (outcome do(t=0), outcome do(t=1), propensity, epsilon)
docstr_tok: ["Fits","the","DragonNet","model","and","then","predicts",".","Args",":","X","(","np.matrix","or","np.array","or","pd.Dataframe",")",":","a","feature","matrix","treatment","(","np.array","or","pd.Series",")",":","a","treatment","vector","y","(","np.array","or","pd.Series",")",":","an","outcome","vector","return_components","(","bool",",","optional",")",":","whether","to","return","Returns",":","(","np.array",")",":","predictions","based","on","return_components","flag","if","return_components=False","(","default",")",",","each","row","is","treatment","effect","if","return_components=True",",","each","row","is","(","outcome","do","(","t=0",")",",","outcome","do","(","t=1",")",",","propensity",",","epsilon",")"]
code: def fit_predict(
      self, X, treatment, y, p=None, return_components=False
  ):
      """
      Fits the DragonNet model and then predicts.
  
      Args:
          X (np.matrix or np.array or pd.Dataframe): a feature matrix
          treatment (np.array or pd.Series): a treatment vector
          y (np.array or pd.Series): an outcome vector
          return_components (bool, optional): whether to return
      Returns:
          (np.array): predictions based on return_components flag
              if return_components=False (default), each row is treatment effect
              if return_components=True, each row is (outcome do(t=0), outcome do(t=1), propensity, epsilon)
      """
      self.fit(X, treatment, y)
      return self.predict_tau(X)
  
file_path: causalml/causalml/inference/tf/dragonnet.py
imports: import numpy
  from tensorflow.keras import Input, Model
  from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN
  from tensorflow.keras.layers import Dense, Concatenate
  from tensorflow.keras.optimizers import SGD, Adam
  from tensorflow.keras.regularizers import l2
  from causalml.inference.tf.utils import dragonnet_loss_binarycross, EpsilonLayer, regression_loss, binary_classification_loss, treatment_accuracy, track_epsilon, make_tarreg_loss
  from causalml.inference.meta.utils import convert_pd_to_np
label_desc: Use image node_id 7 for calling the DragonNet obj's underlying member method code with example usage: obj.fit_predict(X, treatment, y, p, return_components) and returns: self