@tf.function
def weight_fn():
    w = tf.Variable(tf.truncated_normal())

    ValueError: A tf.Variable created inside your tf.function has been garbage-collected. Your code needs to keep Python references to variables created inside `tf.function`s.
    
    A common way to raise this error is to create and return a variable only referenced inside your function:
    
    @tf.function
    def f():
      v = tf.Variable(1.0)
      return v
    
    v = f()  # Crashes with this error message!
    
    The reason this crashes is that @tf.function annotated function returns a **`tf.Tensor`** with the **value** of the variable when the function is called rather than the variable instance itself. As such there is no code holding a reference to the `v` created inside the function and Python garbage collects it.
    
    The simplest way to fix this issue is to create variables outside the function and capture them:
    
    v = tf.Variable(1.0)
    
    @tf.function
    def f():
      return v
    
    f()  # <tf.Tensor: numpy=1.>
    v.assign_add(1.)
    f()  # <tf.Tensor: numpy=2.>

w1 = tf.Variable(tf.truncated_normal())
w2 = tf.Variable(tf.truncated_normal())
w3 = tf.Variable(tf.truncated_normal())
......
w50 = tf.Variable(tf.truncated_normal())