Now that you’ve started learning how to use asymptotic notation to measure the runtime of a function, let’s practice with Python!

When analyzing the runtime of a function, it’s necessary to check the number of iterations the loop will perform based on the size of the input.

The `count`

function on the left takes in a positive integer of size `N`

and returns the number of times it takes to divide `N`

by `2`

until `N`

reaches `1`

.

We can analyze the runtime of this function by counting the number of iterations the `while`

loop will perform based on the size of the input.

### Instructions

**1.**

Change the values of `num_iterations_1`

, `num_iterations_2`

, `num_iterations_4`

, `num_iterations_32`

, and `num_iterations_64`

to the number of iterations the `while`

loop will perform when `N`

is respectively `1`

, `2`

, `4`

, `32`

, and `64`

.

**2.**

Do you notice a pattern forming? With `N`

being divided by `2`

each iteration, we can use that to establish a big O runtime.

Change the value of `runtime`

to whichever one of these values you think the big O runtime is:

`"1"`

`"N"`

`"log N"`

`"N log N"`

`"N^2"`

`"2^N"`

`"N!"`