code: def dragonnet_loss_binarycross(concat_true, concat_pred):
      """
      Implements regression + classification loss in one wrapper function.
  
      Args:
          - concat_true (tf.tensor): tensor of true samples, with shape (n_samples, 2)
                                     Each row in concat_true is comprised of (y, treatment)
          - concat_pred (tf.tensor): tensor of predictions, with shape (n_samples, 4)
                                     Each row in concat_pred is comprised of (y0, y1, propensity, epsilon)
      Returns:
          - (float): aggregated regression + classification loss
      """
      return regression_loss(
          concat_true, concat_pred
      ) + binary_classification_loss(concat_true, concat_pred)
  
file_path: causalml/causalml/inference/tf/utils.py
imports: import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras.layers import Layer
  from tensorflow.keras.metrics import binary_accuracy
label_desc: Use image node_id 3 for calling a global function with example usage: dragonnet_loss_binarycross(concat_true, concat_pred) and returns: unknown