When you need `lifespan` to run in your tests, you can use the `TestClient` with a `with` statement:

{* ../../docs_src/app_testing/tutorial004.py hl[9:15,18,27:28,30:32,41:43] *}


You can read more details about the ["Running lifespan in tests in the official Starlette documentation site."](https://www.starlette.io/lifespan/#running-lifespan-in-tests)

        // Fill with pattern to verify we read correct bytes
        for (int i = 0; i < buffer.length; i++) {
            buffer[i] = (byte) (i + 1);
        }

        // Decode
        int bytesDecoded = smbInfoAllocation.decode(buffer, 0, buffer.length);

        // Verify we read exactly 20 bytes (4 + 4 + 4 + 4 + 4)
        // Note: bytesPerSect is read as Int2 but 4 bytes are consumed (padding)

# Templating - `Jinja2Templates`

You can use the `Jinja2Templates` class to render Jinja templates.

Read more about it in the [FastAPI docs for Templates](https://fastapi.tiangolo.com/advanced/templates/).

You can import it directly from `fastapi.templating`:

```python
from fastapi.templating import Jinja2Templates
```

name: ARM CI Extended

on:
  push:
    tags:
      - v2.**
  schedule:
    - cron: '0 4 * * *'

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      fail-fast: false
      matrix:

#       ./any.sh
#       ...
set -exo pipefail
cd "$(dirname "$0")/../../"  # tensorflow/
# Any request that includes "nightly_upload" should just use the
# local multi-cache (public read-only cache + disk cache) instead.
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
if [[ -n "${TF_ANY_EXTRA_ENV:-}" ]]; then
  export TFCI="$TFCI,$TF_ANY_EXTRA_ENV"
fi

```

::: fastapi.UploadFile
    options:
        members:
            - file
            - filename
            - size
            - headers
            - content_type
            - read
            - write
            - seek

        `"LOCAL"` (case insensitive). If `"AUTO"`, tf.data service runtime
        decides which workers to read from. If `"ANY"`, TF workers read from any
        tf.data service workers. If `"LOCAL"`, TF workers will only read from
        local in-processs tf.data service workers. `"AUTO"` works well for most
        cases, while users can specify other targets. For example, `"LOCAL"`

name: 'Auto Assign PR to Author'
on:
  pull_request:
    types: [opened]

permissions: {}

jobs:
  add-reviews:
    permissions:
      contents: read  # for kentaro-m/auto-assign-action to fetch config file
      pull-requests: write  # for kentaro-m/auto-assign-action to assign PR reviewers
    runs-on: ubuntu-latest
    steps:

                        expressionDocumentation.putAll(doco);
                    }
                } catch (IOException e) {
                    throw new ExpressionDocumentationException(
                            "Failed to read documentation for expression root: " + root, e);
                } catch (XmlPullParserException e) {
                    throw new ExpressionDocumentationException(

name: "Conflict detector"
on:
  push:
  pull_request_target:
    types: [synchronize]

jobs:
  main:
    permissions:
      contents: read
      pull-requests: write
    runs-on: ubuntu-latest
    steps:
      - name: Check if PRs have merge conflicts
        uses: eps1lon/actions-label-merge-conflict@v3
        with:
          dirtyLabel: "conflicts"
          repoToken: "${{ secrets.GITHUB_TOKEN }}"