Training with a loss function consisting of two part based on two different dataset

Question

The loss function is defined as shown in the link The training is based on two different dataset i and k, which corresponds to the two different parts of the loss function. I would like to train using

for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(total_len1/batch_size)
        # Loop over all batches
        for i in range(total_batch-1):
            batch_x = X_train1[i*batch_size:(i+1)*batch_size]
            batch_y = Y_train1[i*batch_size:(i+1)*batch_size]
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c, p = sess.run([optimizer, cost, pred], feed_dict={x: batch_x,
                                                          y: batch_y})
            # Compute average loss
            avg_cost += c / total_batch

But I am struggling with how to implement it for training with such loss function. Thanks in advance.