If you change the function back the result will already be in the Streamlit cache from a previous run. Try it out!
Example 4: When an inner function changes
Let's make our cached function depend on another function internally:
```python
import streamlit as st
import time
def inner_func(a, b):
    st.write("inner_func(", a, ",", b, ") ran")
    return a * b
@st.cache(suppress_st_warning=True)
def expensive_computation(a, b):
    st.write("Cache miss: expensive_computation(", a, ",", b, ") ran")
    time.sleep(2)  # This makes the function take 2s to run
    return inner_func(a, b) + 1
a = 2
b = 210
res = expensive_computation(a, b)
st.write("Result:", res)
```
What you see is the usual:
The first run results in a cache miss.
Every subsequent rerun results in a cache hit.
But now let's try modifying the inner_func():
