I have read about BERT model and came to know that it has 2 training objectives 1.) Masked Language modeling 2.) Next sentence prediction. So, I wonder how is this implemented practically for our custom transformer, if i want to have some 4-5 training objectives for my custom transformer. Is implementation of 2 training objective function same as like this? or do we have an other approach for implementation:

```
import tensorflow as tf
from tensorflow.keras import layers
# Define your model architecture
inputs = layers.Input(shape=(10,))
hidden = layers.Dense(16, activation='relu')(inputs)
output1 = layers.Dense(1, activation='sigmoid')(hidden) # Binary classification output
output2 = layers.Dense(1)(hidden) # Regression output
model = tf.keras.Model(inputs=inputs, outputs=[output1, output2])
# Define the loss functions for each objective
loss_fn1 = tf.keras.losses.BinaryCrossentropy()
loss_fn2 = tf.keras.losses.MeanSquaredError()
# Define the optimizer
optimizer = tf.keras.optimizers.Adam()
@tf.function
def train_step(inputs, labels1, labels2):
with tf.GradientTape() as tape:
# Forward pass
predictions1, predictions2 = model(inputs, training=True)
# Compute the losses for each objective
loss1 = loss_fn1(labels1, predictions1)
loss2 = loss_fn2(labels2, predictions2)
# Combine the losses
total_loss = loss1 + loss2
# Compute gradients and update weights
gradients = tape.gradient(total_loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# Generate some dummy data for training
train_inputs = tf.random.normal((100, 10))
train_labels1 = tf.random.uniform((100, 1), minval=0, maxval=2, dtype=tf.int32)
train_labels2 = tf.random.normal((100, 1))
# Training loop
for epoch in range(10):
for inputs, labels1, labels2 in zip(train_inputs, train_labels1, train_labels2):
train_step(tf.expand_dims(inputs, 0), tf.expand_dims(labels1, 0), tf.expand_dims(labels2, 0))
# Perform predictions
test_inputs = tf.random.normal((10, 10))
predictions1, predictions2 = model(test_inputs, training=False)
```