code: def binary_classification_loss(concat_true, concat_pred):
      """
      Implements a classification (binary cross-entropy) 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): binary cross-entropy loss
      """
      t_true = concat_true[:, 1]
      t_pred = concat_pred[:, 2]
      t_pred = (t_pred + 0.001) / 1.002
      losst = tf.reduce_sum(K.binary_crossentropy(t_true, t_pred))
  
      return losst
  
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 1 for calling a global function with example usage: binary_classification_loss(concat_true, concat_pred) and returns: losst