type User struct {
	gorm.Model
	Name      string
	Age       uint
	Birthday  *time.Time
	Account   Account
	Pets      []*Pet
	NamedPet  *Pet
	Toys      []Toy   `gorm:"polymorphic:Owner"`
	Tools     []Tools `gorm:"polymorphicType:Type;polymorphicId:CustomID"`
	CompanyID *int
	Company   Company
	ManagerID *uint
	Manager   *User
	Team      []User     `gorm:"foreignkey:ManagerID"`

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

### Examples of Replication Tools and Strategies

There can be several approaches to achieve this, and I'll tell you more about specific strategies in the next chapters, for example when talking about Docker and containers.

  }

  private fun updateMaxConcurrentStreams(
    connection: Http2Connection,
    amount: Int,
  ) {
    val settings = Settings()
    settings[Settings.MAX_CONCURRENT_STREAMS] = amount
    connection.readerRunnable.applyAndAckSettings(true, settings)
    assertThat(connection.peerSettings[Settings.MAX_CONCURRENT_STREAMS]).isEqualTo(amount)
    taskFaker.runTasks()
  }

	}
	var crossMounts mountInfos
	for _, mount := range mts {
		// Add a separator to indicate that this is a proper mount-point.
		// This is to avoid a situation where prefix is '/tmp/fsmount'
		// and mount path is /tmp/fs. In such a scenario we need to check for
		// `/tmp/fs/` to be a common prefix amount other mounts.
		mpath := strings.TrimSuffix(mount.Path, "/") + "/"
		ppath := strings.TrimSuffix(path, "/") + "/"

      color: *color
      line:
        amount: 3
        height: 1.25
      font:
        family: *font_family
        style: Bold

  # Page description
  - size: { width: 832, height: 64 }
    offset: { x: 64, y: 512 }
    typography:
      content: *page_description
      align: start
      color: *color
      line:
        amount: 2
        height: 1.5
      font:

		tok := p.next()
		ext = tok.String()
	}
	if p.peek() == lex.LSH {
		// parses left shift amount applied after extension: <<Amount
		p.get(lex.LSH)
		tok := p.get(scanner.Int)
		amount, err := strconv.ParseInt(tok.String(), 10, 16)
		if err != nil {
			p.errorf("parsing left shift amount: %s", err)
		}
		num = int16(amount)
	} else if p.peek() == '[' {
		// parses an element: [Index]
		p.get('[')

  }

  /**
   * Sends the given method call to this thread.
   *
   * @throws TimeoutException if this thread does not accept the request within a reasonable amount
   *     of time
   */
  private void sendRequest(String methodName, Object... arguments) throws Exception {
    if (!requestQueue.offer(

			Optional:    true,
			Type:        "string",
		},
		config.HelpKV{
			Key:         browserHSTSSeconds,
			Description: `set Strict-Transport-Security 'max-age' amount of seconds value` + defaultHelpPostfix(browserHSTSSeconds),
			Optional:    true,
			Type:        "number",
		},
		config.HelpKV{
			Key:         browserHSTSIncludeSubdomains,

  @CanIgnoreReturnValue
  public FakeTicker setAutoIncrementStep(long autoIncrementStep, TimeUnit timeUnit) {
    checkArgument(autoIncrementStep >= 0, "May not auto-increment by a negative amount");
    this.autoIncrementStepNanos = timeUnit.toNanos(autoIncrementStep);
    return this;
  }

  /**
   * Sets the increment applied to the ticker whenever it is queried.
   *

    * So, by using FastAPI you are saving development time, bugs, lines of code, and you would probably get the same performance (or better) you would if you didn't use it (as you would have to implement it all...