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You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

=== "Python 3.10+"

    ```Python hl_lines="16  21"
    {!> ../../../docs_src/response_model/tutorial001_01_py310.py!}
    ```

=== "Python 3.9+"

message HorizontalPodAutoscalerSpec {
  // reference to scaled resource; horizontal pod autoscaler will learn the current resource consumption
  // and will set the desired number of pods by using its Scale subresource.
  optional CrossVersionObjectReference scaleTargetRef = 1;

  // minReplicas is the lower limit for the number of replicas to which the autoscaler
  // can scale down.  It defaults to 1 pod.  minReplicas is allowed to be 0 if the

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

// ScaleSpec describes the attributes of a scale subresource
message ScaleSpec {
  // replicas is the number of observed instances of the scaled object.
  // +optional
  optional int32 replicas = 1;

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/autoscaling/v2";

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to

        findLatest("scala-library", "org.scala-lang:scala-library:2.13.+", versionProperties)
        val scalaVersion = VersionNumber.parse(versionProperties["scala-library"] as String)
        versionProperties["scala"] = "${scalaVersion.major}.${scalaVersion.minor}"

        // The latest released version is 2.0.0-M1, which is excluded by "don't use snapshot" strategy

*   For C++ API users: `IsLegacyScalar` and `IsLegacyVector` are now gone from
    `TensorShapeUtils` since TensorFlow is scalar strict within Google (for
    example, the shape argument to `tf.reshape` can't be a scalar anymore). The
    open source release was already scalar strict, so outside Google `IsScalar`
    and `IsVector` are exact replacements.
*   The following files are being removed from `tensorflow/core/public/`:

### Docs

* 📝 Tweak default suggested configs for generating clients. PR [#10736](https://github.com/tiangolo/fastapi/pull/10736) by [@tiangolo](https://github.com/tiangolo).

### Internal

* 🔧 Update sponsors, add Scalar. PR [#10728](https://github.com/tiangolo/fastapi/pull/10728) by [@tiangolo](https://github.com/tiangolo).

            k <= bias -> TMIN
            k >= bias + TMAX -> TMAX
            else -> k - bias
          }
        if (digit < t) break
        val scaleW = BASE - t
        if (w > Int.MAX_VALUE / scaleW) return false // Prevent overflow.
        w *= scaleW
      }
      bias = adapt(i - oldi, codePoints.size + 1, oldi == 0)
      val deltaN = i / (codePoints.size + 1)

### Autoscaling

- Horizontal Pod Autoscaling can now scale targets up even when one or more metrics are invalid/unavailable, as long as one metric indicates a scale up should occur. ([#78503](https://github.com/kubernetes/kubernetes/pull/78503), [@gjtempleton](https://github.com/gjtempleton))


### AWS