text: import tensorflow as tf
  import tensorflow_probability as tfp
  import numpy as np
  
  ndims=10
  start = np.arange(ndims, 0, -1, dtype='float64')
  
  A = tf.Variable(initial_value=start, name='A')
  B = tf.Variable(initial_value=start, name='B')
  
  # @tf.function
  def Loss():
      return A * B
  
  # myDict = {'A':A,'B':B}
  @tf.function
  def myLoss():
      print('Hello World')
      return tf.reduce_sum(Loss())
  
  opt = tf.keras.optimizers.SGD(learning_rate=0.01)
  for _ in range(100):
      opt.minimize(loss=myLoss, var_list=[B])
  
  #print(A.numpy())
  #print(B.numpy())
  