// and NonResourceAuthorizationAttributes must be set
message SelfSubjectAccessReviewSpec {
  // ResourceAuthorizationAttributes describes information for a resource access request
  // +optional
  optional ResourceAttributes resourceAttributes = 1;

  // NonResourceAttributes describes information for a non-resource access request
  // +optional
  optional NonResourceAttributes nonResourceAttributes = 2;
}

  // Read-only.
  // More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status
  // +optional
  optional DaemonSetStatus status = 3;
}

// DaemonSetCondition describes the state of a DaemonSet at a certain point.
message DaemonSetCondition {
  // Type of DaemonSet condition.
  optional string type = 1;

  // Status of the condition, one of True, False, Unknown.

### Concurrency and Burgers

This idea of **asynchronous** code described above is also sometimes called **"concurrency"**. It is different from **"parallelism"**.

**Concurrency** and **parallelism** both relate to "different things happening more or less at the same time".

Si necesitas un repaso de 2 minutos de cómo usar los tipos de Python (así no uses FastAPI) prueba el tutorial corto: [Python Types](python-types.md){.internal-link target=_blank}.

Escribes Python estándar con tipos así:

```Python
from datetime import date

from pydantic import BaseModel

# Declaras la variable como un str
# y obtienes soporte del editor dentro de la función

import "k8s.io/apimachinery/pkg/runtime/schema/generated.proto";

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/certificates/v1beta1";

// Describes a certificate signing request
message CertificateSigningRequest {
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.ObjectMeta metadata = 1;

*   Add HW acceleration support for `topK_v2`.
*   Add new `TypeSpec` classes.
*   CloudBigtable version updated to v0.10.0.
*   Expose `Head` as public API.
*   Update docstring for gather to properly describe the non-empty `batch_dims`
    case.
*   Added `tf.sparse.from_dense` utility function.
*   Improved ragged tensor support in `TensorFlowTestCase`.

  // +optional
  optional HorizontalPodAutoscalerStatus status = 3;
}

// HorizontalPodAutoscalerCondition describes the state of
// a HorizontalPodAutoscaler at a certain point.
message HorizontalPodAutoscalerCondition {
  // type describes the current condition
  optional string type = 1;

  // status is the status of the condition (True, False, Unknown)
  optional string status = 2;

  // +optional
  optional HorizontalPodAutoscalerStatus status = 3;
}

// HorizontalPodAutoscalerCondition describes the state of
// a HorizontalPodAutoscaler at a certain point.
message HorizontalPodAutoscalerCondition {
  // type describes the current condition
  optional string type = 1;

  // status is the status of the condition (True, False, Unknown)
  optional string status = 2;

That extra info will be added as-is to the output **JSON Schema** for that model, and it will be used in the API docs.

=== "Pydantic v2"

    In Pydantic version 2, you would use the attribute `model_config`, that takes a `dict` as described in <a href="https://docs.pydantic.dev/latest/usage/model_config/" class="external-link" target="_blank">Pydantic's docs: Model Config</a>.

  // +optional
  optional DeploymentSpec spec = 2;

  // Most recently observed status of the Deployment.
  // +optional
  optional DeploymentStatus status = 3;
}

// DeploymentCondition describes the state of a deployment at a certain point.
message DeploymentCondition {
  // Type of deployment condition.
  optional string type = 1;

  // Status of the condition, one of True, False, Unknown.