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)

ed){var p1=axis.c2p(c1[axis.direction]),p2=axis.c2p(c2[axis.direction]);r[name]={from:Math.min(p1,p2),to:Math.max(p1,p2)}}});return r}function triggerSelectedEvent(){var r=getSelection();plot.getPlaceholder().trigger("plotselected",[r]);if(r.xaxis&&r.yaxis)plot.getPlaceholder().trigger("selected",[{x1:r.xaxis.from,y1:r.yaxis.from,x2:r.xaxis.to,y2:r.yaxis.to}])}function clamp(min,value,max){return value<min?min:value>max?max:value}function setSelectionPos(pos,e){var o=plot.getOptions();var offset...

---

"_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._"

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

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.

<img src="/img/tutorial/websockets/image02.png">

And your **FastAPI** application with WebSockets will respond back:

<img src="/img/tutorial/websockets/image03.png">

You can send (and receive) many messages:

<img src="/img/tutorial/websockets/image04.png">

And all of them will use the same WebSocket connection.

## Using `Depends` and others

## Future

By this point, it's already clear that **FastAPI** with its ideas is being useful for many people.

It is being chosen over previous alternatives for suiting many use cases better.

Many developers and teams already depend on **FastAPI** for their projects (including me and my team).

But still, there are many improvements and features to come.

**FastAPI** has a great future ahead.

  SmallVector<double, 4> min_value, max_value;
  const auto mins = dyn_cast<DenseFPElementsAttr>(min);
  const auto maxs = dyn_cast<DenseFPElementsAttr>(max);
  if (mins && maxs) {
    min_value.reserve(mins.getNumElements());
    max_value.reserve(maxs.getNumElements());
    for (auto it = mins.begin(); it != mins.end(); ++it) {
      min_value.push_back(FloatAttr::getValueAsDouble(*it));
    }