on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,

// they are only defined to be used in this file alone.
type grantee struct {
	XMLNS       string `xml:"xmlns:xsi,attr"`
	XMLXSI      string `xml:"xsi:type,attr"`
	Type        string `xml:"Type"`
	ID          string `xml:"ID,omitempty"`
	DisplayName string `xml:"DisplayName,omitempty"`
	URI         string `xml:"URI,omitempty"`
}

type grant struct {
	Grantee    grantee `xml:"Grantee"`
	Permission string  `xml:"Permission"`
}

    }

    /**
     * Check if a lease was granted in this response
     * @return true if a lease was granted
     */
    public boolean isLeaseGranted() {
        return getLeaseV1Context() != null || getLeaseV2Context() != null;
    }

    /**
     * Check if a durable handle was granted in this response
     * @return true if a durable handle was granted
     */
    public boolean isDurableHandleGranted() {

Y funcionará y será útil **durante el desarrollo**.

Pero si pierdes la conexión con el servidor, el **proceso en ejecución** probablemente morirá.

## Sobrescribir dependencias durante las pruebas { #overriding-dependencies-during-testing }

Hay algunos escenarios donde podrías querer sobrescribir una dependencia durante las pruebas.

No quieres que la dependencia original se ejecute (ni ninguna de las sub-dependencias que pueda tener).

can regenerate automatically from other parts of the Corresponding
Source.

  The Corresponding Source for a work in source code form is that
same work.

  2. Basic Permissions.

  All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met.  This License explicitly affirms your unlimited

        this.initialCredits = initialCredits;
    }

    /**
     * Get number of credits granted by peer
     *
     * @return granted credits
     */
    public int getCreditsGranted() {
        return creditsGranted;
    }

    /**
     * Set number of credits granted by peer
     *
     * @param creditsGranted credits granted
     */
    public void setCreditsGranted(int creditsGranted) {

| IMPORTANT |
| -------------------------- |

	refreshInterval      time.Duration
	lockRetryMinInterval time.Duration
}

// Granted - represents a structure of a granted lock.
type Granted struct {
	index   int
	lockUID string // Locked if set with UID string, unlocked if empty
}

func (g *Granted) isLocked() bool {
	return isLocked(g.lockUID)
}

func isLocked(uid string) bool {
	return len(uid) > 0
}

* **Aprendizado de Máquina**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.
* **Deep Learning**: Esse é um subcampo do Aprendizado de Máquina, então, o mesmo se aplica. A diferença é que não há apenas uma grande planilha com números para multiplicar, mas um grande conjunto delas, e em muitos casos, você utiliza um processador especial para construir e/ou usar esses modelos.