// 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

// 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: