code: def regression_loss(concat_true, concat_pred):
      """
      Implements a regression (squared error) loss function for DragonNet architecture.
  
      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 loss
      """
      y_true = concat_true[:, 0]
      t_true = concat_true[:, 1]
  
      y0_pred = concat_pred[:, 0]
      y1_pred = concat_pred[:, 1]
  
      loss0 = tf.reduce_sum(
          (1.0 - t_true) * tf.square(y_true - y0_pred)
      )
      loss1 = tf.reduce_sum(t_true * tf.square(y_true - y1_pred))
  
      return loss0 + loss1
  
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 2 for calling a global function with example usage: regression_loss(concat_true, concat_pred) and returns: unknown