public void testHtmlEscaper() throws Exception {
    assertEquals("xxx", htmlEscaper().escape("xxx"));
    assertEquals(""test"", htmlEscaper().escape("\"test\""));
    assertEquals("'test'", htmlEscaper().escape("\'test'"));
    assertEquals("test & test & test", htmlEscaper().escape("test & test & test"));
    assertEquals("test &lt;&lt; 1", htmlEscaper().escape("test << 1"));

# 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]
```

              }
            }
          }
        }
      }
    }
  }
}

boolean isDeployedBranch() {
  return env.BRANCH_NAME == 'master' || env.BRANCH_NAME == 'maven-4.0.x' || env.BRANCH_NAME == 'maven-3.9.x'
}

/**
 * To other developers, if you are using this method above, please use the following syntax.
 * By default this method does NOT execute ITs anymore, just "install".
 *

Ниже изложена основная идея того, как могут выглядеть эти модели с полями для паролей, а также описаны места, где они используются:

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

### Про `**user_in.dict()`

#### `.dict()` из Pydantic

`user_in` - это Pydantic-модель класса `UserIn`.

У Pydantic-моделей есть метод `.dict()`, который возвращает `dict` с данными модели.

    placeholder: e.g., Linux Ubuntu 16.04 validations: required: false

-   type: input id: Python attributes: label: Python version description:
    placeholder: e.g., 3.9 validations: required: false

-   type: input id: Bazel attributes: label: Bazel version description: if
    compiling from source placeholder: validations: required: false

Hier der generelle Weg, wie die Modelle mit ihren Passwort-Feldern aussehen könnten, und an welchen Orten sie verwendet werden würden.

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

/// info

In Pydantic v1 hieß diese Methode `.dict()`, in Pydantic v2 wurde sie deprecated (aber immer noch unterstützt) und in `.model_dump()` umbenannt.

    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",

## Múltiples modelos

Aquí tienes una idea general de cómo podrían ser los modelos con sus campos de contraseña y los lugares donde se utilizan:

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

/// info | Información

En Pydantic v1 el método se llamaba `.dict()`, fue deprecado (pero aún soportado) en Pydantic v2, y renombrado a `.model_dump()`.

    id: Mobile
    attributes:
      label: Mobile device
      placeholder: e.g., Linux Ubuntu 16.04
  - type: input
    id: Python
    attributes:
      label: Python version
      placeholder: e.g., 3.9
  - type: input
    id: Bazel
    attributes:
      label: Bazel version
      description: If compiling from source
  - type: input
    id: Compiler
    attributes:
      label: GCC/compiler version