changing learning rate over epoch in tensorflow 2 using custom training loop

I want to optimize 2 loss components separately , so thinking of running two optimizer for them.

is changing learning rate over epoch in tensorflow 2 using custom training loop,in this way ok?

    batch_size = 4
    epochs = 50
    myLearnRate1=1e-4
    myLearnRate2=1e-4  
    X_train,X_test=train_data,val_data

    for epoch in range(0, epochs):
      train_loss=[]
      for i in range(0, len(X_train) // batch_size):
          X = X_train[i * batch_size:min(len(X_train), (i+1) * batch_size)]
          Image,Mask_p,Mask_n=create_mask(X)
                    
          Lr1= myLearnRate1 /(1 + (epoch/37))    
          Lr2= myLearnRate2 /(1 + (epoch/37))   
          optimizer1 =tf.keras.optimizers.Adam(learning_rate=Lr1)
          optimizer2 =tf.keras.optimizers.Adam(learning_rate=Lr2)
          
          loss=train_on_batch(Image,Mask_p,Mask_n,optimizer1,optimizer2)  

basically i am sending the " optimizer along with different learning rate" in each iteration to training function

training function

def train_on_batch(X_original,X_p,X_n,optimizer1,optimizer2):
  with tf.GradientTape(persistent=True) as tape:
    # Forward pass.
    recon,latent=autoencoder_model([X_original,X_p,X_n],training=True)
    # Loss value for this batch.
    loss_value1_,loss_value2=assymetric_fun(X_original,X_p,X_n,recon)
    loss_value1=-1.0*loss_value1_
    
  # make gradient
  grads1 = tape.gradient(loss_value1, autoencoder_model.trainable_variables)
  grads2 = tape.gradient(loss_value2, autoencoder_model.trainable_variables)
  
  #update weight
  optimizer1.apply_gradients(zip(grads1, autoencoder_model.trainable_variables))
  optimizer2.apply_gradients(zip(grads2, autoencoder_model.trainable_variables))
  
  return loss_value1_+loss_value2