* @param allPageCount The total number of pages.
     */
    public void setAllPageCount(final int allPageCount) {
        this.allPageCount = allPageCount;
    }

    /**
     * Checks if a previous page exists.
     * @return true if a previous page exists, false otherwise.
     */
    public boolean isExistPrePage() {
        return existPrePage;
    }

    /**

     *
     * @param allPageCount the total page count
     */
    public void setAllPageCount(final int allPageCount) {
        this.allPageCount = allPageCount;
    }

    /**
     * Checks if there is a previous page available.
     *
     * @return true if previous page exists, false otherwise
     */
    public boolean isExistPrePage() {
        return existPrePage;
    }

    /**

Check the in-code annotation tips above to see more specific details.

## Learn More { #learn-more }

You can also combine `dataclasses` with other Pydantic models, inherit from them, include them in your own models, etc.

permissions:
  contents: read

env:
  GRADLE_OPTS: "-Dorg.gradle.jvmargs=-Xmx4g -Dorg.gradle.daemon=false -Dkotlin.incremental=false"

jobs:
  test_containers:
    permissions:
      checks: write # for actions/upload-artifact
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/master' || contains(github.event.pull_request.labels.*.name, 'containers')

    steps:
      - name: Checkout

    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    name: "validate"
    runs-on: ubuntu-latest
    steps:
      - name: Check out a copy of the repository
        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0

      - name: Check whether the citation metadata from CITATION.cff is valid
        uses: citation-file-format/cffconvert-github-action@4cf11baa70a673bfdf9dad0acc7ee33b3f4b6084 # v2.0.0

    - ineffassign
    - misspell
    - revive
    - staticcheck
    - unconvert
    - unused
    - usetesting
    - whitespace
  settings:
    misspell:
      locale: US
    staticcheck:
      checks:
        - all
        - -SA1008
        - -SA1019
        - -SA4000
        - -SA9004
        - -ST1000
        - -ST1005
        - -ST1016
        - -U1000
  exclusions:
    generated: lax

inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack
scenario would require a malicious user to be able to control the model which as

     *
     * @param searchRequestParams The search request parameters
     * @param data The search render data to populate with results
     * @param userBean Optional user information for permission checking
     */
    public void search(final SearchRequestParams searchRequestParams, final SearchRenderData data,
            final OptionalThing<FessUserBean> userBean) {

* e2e: adapt kubelet_perf.go to use the new summary metrics API ([#24003](https://github.com/kubernetes/kubernetes/pull/24003), [@yujuhong](https://github.com/yujuhong))
* e2e: fix error checking in kubelet stats ([#24205](https://github.com/kubernetes/kubernetes/pull/24205), [@yujuhong](https://github.com/yujuhong))

You need to import `WSGIMiddleware`.

Then wrap the WSGI (e.g. Flask) app with the middleware.

And then mount that under a path.

{* ../../docs_src/wsgi/tutorial001.py hl[2:3,3] *}

## Check it { #check-it }

Now, every request under the path `/v1/` will be handled by the Flask application.

And the rest will be handled by **FastAPI**.