Why does tf.random.uniform generate duplicates inside a function but not in the main program?

[Touami-M]

I was trying to understand how seed generation works (mainly with functions), my confusion is if I run this bit of code in my main program :

a = tf.random.uniform([1], seed=1)
b = tf.random.uniform([1], seed=1)
print(a)
print(b)

I get this output

tf.Tensor([0.2390374], shape=(1,), dtype=float32)

tf.Tensor([0.22267115], shape=(1,), dtype=float32)

two different tensors (obviously) but if I try to call it from a function :

@tf.function
def foo():
  a = tf.random.uniform([1], seed=1)
  b = tf.random.uniform([1], seed=1)
  print(a)
  print(b)
  return a, b

foo()

I get this:

Tensor("random_uniform/RandomUniform:0", shape=(1,), dtype=float32)

Tensor("random_uniform_1/RandomUniform:0", shape=(1,), dtype=float32)

(<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.2390374], dtype=float32)>,

<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.2390374], dtype=float32)>)

There's two things I don't get:

1. Why did the two prints inside the function show different output to the print from the return value of the function, in other words why was no value (numpy=array...) shown in the first two lines compared to the last two?
2. Why are a and b equal?