If you can use the **latest versions of Python**, use the examples for the latest version, those will have the **best and simplest syntax**, for example, "**Python 3.10+**".

#### List { #list }

For example, let's define a variable to be a `list` of `str`.

//// tab | Python 3.9+

    - name: Report list of changed files
      run: |
        echo Changed files: ${{ steps.get_file_changes.outputs.files }}
    - name: Set up Python 3.9
      uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
      with:
        python-version: "3.9"
    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint==2.13.9 numpy wheel

    target-branch: "maven-4.0.x"
    labels:
      - "mvn40"
      - "dependencies"

  - package-ecosystem: "maven"
    directory: "/"
    schedule:
      interval: "daily"
    target-branch: "maven-3.9.x"
    labels:
      - "mvn3"
      - "dependencies"

  - package-ecosystem: "github-actions"
    directory: "/"
    schedule:
      interval: "daily"

  - package-ecosystem: "github-actions"

Here's a general idea of how the models could look like with their password fields and the places where they are used:

{* ../../docs_src/extra_models/tutorial001_py310.py hl[7,9,14,20,22,27:28,31:33,38:39] *}


/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

```
rm -rf /tmp/etcd-data.tmp && mkdir -p /tmp/etcd-data.tmp && \
  podman rmi gcr.io/etcd-development/etcd:v3.3.9 || true && \
  podman run \
  -p 2379:2379 \
  -p 2380:2380 \
  --mount type=bind,source=/tmp/etcd-data.tmp,destination=/etcd-data \
  --name etcd-gcr-v3.3.9 \
  gcr.io/etcd-development/etcd:v3.3.9 \
  /usr/local/bin/etcd \
  --name s1 \
  --data-dir /etcd-data \
  --listen-client-urls http://0.0.0.0:2379 \

        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: actions/checkout@v5
      - uses: actions/setup-python@v5
        with:
          python-version: '3.9'
      - name: Setup uv
        uses: astral-sh/setup-uv@v6
        with:
          version: "0.4.15"
          enable-cache: true
          cache-dependency-glob: |
            requirements**.txt

    squash: true
    merge: false
    rebase: true
  autolink_jira:
    - MNG
  pull_requests:
    del_branch_on_merge: true
  protected_branches:
    master: {}
    maven-4.0.x: {}
    maven-3.9.x: {}
    maven-3.8.x: {}
    maven-3.0.x: {}
    pre-reset-master: {}
  features:
    issues: true
notifications:
  commits: ******@****.***
  issues: ******@****.***

# Note that numpy 2.1.0 does not support python 3.9
numpy >= 2.0.0, < 2.2.0
wheel ~= 0.41.2
h5py >= 3.11.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras

* If you are in a Python version lower than 3.9, import their equivalent version from the `typing` module
* Pass the internal type(s) as "type parameters" using square brackets: `[` and `]`

In Python 3.9 it would be:

```Python
my_list: list[str]
```

In versions of Python before 3.9, it would be:

```Python
from typing import List

my_list: List[str]
```

    local_wheel_dist_folder = "dist",
    local_wheel_inclusion_list = [
        "tensorflow*",
        "tf_nightly*",
    ],
    local_wheel_workspaces = ["//:WORKSPACE"],
    requirements = {
        "3.9": "//:requirements_lock_3_9.txt",
        "3.10": "//:requirements_lock_3_10.txt",
        "3.11": "//:requirements_lock_3_11.txt",
        "3.12": "//:requirements_lock_3_12.txt",
        "3.13": "//:requirements_lock_3_13.txt",