with:
          signoff: true
          sign-commits: true
          commit-message: "Update jdks.yaml"
          add-paths: .teamcity/jdks.yaml
          title: "Update jdks.yaml"
          body: "This PR updates JDK to the latest versions available in `.teamcity/jdks.yaml`."
          delete-branch: true
          branch: "devprod/update-jdks"
          branch-suffix: timestamp
          labels: |
            in:building-gradle

#
# To prepare an update for a new Go release,
# consult https://www.iana.org/time-zones for the latest versions,
# update CODE and DATA below, and then run
#
#	./update.bash -commit
#
# That will prepare the files and create the commit.
#
# To review such a commit (as the reviewer), use:
#
#	git codereview change NNNNNN   # CL number
#	cd lib/time
#	./update.bash
#

# This Workflow updates tensorflow/tools/toolchains/remote_config/configs.bzl
# to reference the most recent versions of the SIG Build Docker images.
name: Update RBE Configs
on:
  workflow_dispatch:

permissions:
  contents: read

jobs:
  rbe:
    name: Update RBE Configs
    runs-on: ubuntu-latest

        with:
          commit-message: Update test buckets
          branch: devprod/update-test-buckets
          branch-suffix: timestamp
          delete-branch: true
          title: 'Update test buckets'
          signoff: true
          sign-commits: true
          labels: |
            in:building-gradle
            @dev-productivity
          body: |

          branch-suffix: timestamp
          delete-branch: true
          title: 'Update performance test durations'
          signoff: true
          sign-commits: true
          labels: |
            in:building-gradle
            @dev-productivity
          body: |

### Using Pydantic's `update` parameter { #using-pydantics-update-parameter }

Now, you can create a copy of the existing model using `.model_copy()`, and pass the `update` parameter with a `dict` containing the data to update.

Like `stored_item_model.model_copy(update=update_data)`:

{* ../../docs_src/body_updates/tutorial002_py310.py hl[33] *}

### Partial updates recap { #partial-updates-recap }

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Pydantics `update`-Parameter verwenden { #using-pydantics-update-parameter }

Jetzt können Sie eine Kopie des existierenden Modells mittels `.model_copy()` erstellen, wobei Sie dem `update`-Parameter ein `dict` mit den zu ändernden Daten übergeben.

Wie in `stored_item_model.model_copy(update=update_data)`:

{* ../../docs_src/body_updates/tutorial002_py310.py hl[33] *}

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Uso del parámetro `update` de Pydantic { #using-pydantics-update-parameter }

Ahora, puedes crear una copia del modelo existente usando `.model_copy()`, y pasar el parámetro `update` con un `dict` que contenga los datos a actualizar.

/// info | Información

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Usando o parâmetro `update` do Pydantic { #using-pydantics-update-parameter }

Agora, você pode criar uma cópia do modelo existente usando `.model_copy()`, e passar o parâmetro `update` com um `dict` contendo os dados para atualizar.

/// info | Informação

        try (MappingUpdater updater = new MappingUpdater(item)) {
            reload(updater);
        }
    }

    /**
     * Updates an existing character mapping item in the dictionary file.
     *
     * @param item the character mapping item to update
     */
    @Override
    public synchronized void update(final CharMappingItem item) {