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Implementation:Googleapis Python genai Tokens Converters

From Leeroopedia
Knowledge Sources
Domains Data_Transformation, Authentication
Last Updated 2026-02-15 14:00 GMT

Overview

Concrete tool for converting token-related and live connect configuration objects between ML Dev and Vertex AI formats.

Description

The _tokens_converters module contains code-generated converter functions for auth token creation (CreateAuthTokenConfig, CreateAuthTokenParameters) and their associated Live API types (LiveConnectConfig, LiveConnectConstraints, Tool). It maps authentication token parameters and live session constraints between API formats.

Usage

Internal infrastructure. Called automatically when creating auth tokens or configuring live connect constraints.

Code Reference

Source Location

Signature

def _CreateAuthTokenConfig_to_mldev(api_client: BaseApiClient, from_object: Union[dict, object], parent_object: Optional[dict] = None) -> dict[str, Any]: ...
def _CreateAuthTokenParameters_to_mldev(api_client: BaseApiClient, from_object: Union[dict, object], parent_object: Optional[dict] = None) -> dict[str, Any]: ...
def _LiveConnectConfig_to_mldev(api_client: BaseApiClient, from_object: Union[dict, object], parent_object: Optional[dict] = None) -> dict[str, Any]: ...
def _Tool_to_mldev(from_object: Union[dict, object], parent_object: Optional[dict] = None) -> dict[str, Any]: ...

Import

from google.genai import _tokens_converters

I/O Contract

Inputs

Name Type Required Description
api_client BaseApiClient For some converters Client for determining API backend
from_object dict or object Yes Source token/config object to convert
parent_object dict No Parent context for nested conversion

Outputs

Name Type Description
returns dict[str, Any] Converted object in target API format

Usage Examples

# Internal usage - called automatically by the Tokens module
from google.genai._tokens_converters import _CreateAuthTokenConfig_to_mldev

config_dict = _CreateAuthTokenConfig_to_mldev(
    api_client=client._api_client,
    from_object={"uses": 1, "expire_time": "2024-01-01T00:00:00Z"},
)

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