// in this file.
//
// The following statement will instantiate tests from the FooTest test case
// each with parameter values "meeny", "miny", and "moe".

INSTANTIATE_TEST_CASE_P(InstantiationName,
                        FooTest,
                        Values("meeny", "miny", "moe"));

// To distinguish different instances of the pattern, (yes, you
// can instantiate it more then once) the first argument to the

            String min1 = Collections.min(option1.getOptions(), new OptionStringComparator());
            String min2 = Collections.min(option2.getOptions(), new OptionStringComparator());
            // Group opposite option pairs together
            min1 = min1.startsWith(DISABLE_OPTION_PREFIX) ? min1.substring(DISABLE_OPTION_PREFIX.length()) + "-" : min1;

	nextSample uintptr // trigger heap sample after allocating this many bytes
	scanAlloc  uintptr // bytes of scannable heap allocated

	// Allocator cache for tiny objects w/o pointers.
	// See "Tiny allocator" comment in malloc.go.

	// tiny points to the beginning of the current tiny block, or
	// nil if there is no current tiny block.
	//
	// tiny is a heap pointer. Since mcache is in non-GC'd memory,

In many cases you might already know the answer for those questions. 🤓

If you are helping a lot of people with their questions, you will become an official [FastAPI Expert](fastapi-people.md#fastapi-experts){.internal-link target=_blank}. 🎉

func.func @fakeQuantFoldedWithCast() -> (tensor<8xf32>) {
  %in = arith.constant dense<0.0> : tensor<8xf32>
  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<255.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %minc = "tf.Cast"(%mini) : (tensor<f32>) -> tensor<f32>
  %maxc = "tf.Cast"(%maxi) : (tensor<f32>) -> tensor<f32>

func.func @fakeQuantFoldedWithCast() -> (tensor<8xf32>) {
  %in = arith.constant dense<0.0> : tensor<8xf32>
  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %minc = "tf.Cast"(%mini) : (tensor<f32>) -> tensor<f32>
  %maxc = "tf.Cast"(%maxi) : (tensor<f32>) -> tensor<f32>

		_ = min(1)
		_ = min(1, 2.3, 'a')
		_ = min(1, "foo" /* ERROR "mismatched types" */ )
		_ = min(1, 0i /* ERROR "cannot be ordered" */ )
		_ = min(1, 2 /* ERROR "cannot be ordered" */ + 3i )
	)
}

func min2() {
	_ = assert(min(0) == 0)
	_ = assert(min(0, 1) == 0)
	_ = assert(min(0, -10, 123456789) == -10)
	_ = assert(min(-12345678901234567890, 0) == -12345678901234567890)

	_ = assert(min(1, 2.3) == 1)

---

"_Honestly, what you've built looks super solid and polished. In many ways, it's what I wanted **Hug** to be - it's really inspiring to see someone build that._"

So, the thief won't be able to try to use that password in another system (as many users use the same password everywhere, this would be dangerous).

## Install `passlib`

PassLib is a great Python package to handle password hashes.

It supports many secure hashing algorithms and utilities to work with them.

The recommended algorithm is "Bcrypt".

So, install PassLib with Bcrypt:

* There can be **multiple processes** of the **same program** running at the same time.

If you check out the "task manager" or "system monitor" (or similar tools) in your operating system, you will be able to see many of those processes running.