feat: Add GkeCodeExecutor for sandboxed code execution on GKE #non-breaking

Merge https://github.com/google/adk-python/pull/1629

close https://github.com/google/adk-python/issues/2170

### Summary

This PR introduces `GkeCodeExecutor`, a new code executor that provides a secure and scalable method for running LLM-generated code by leveraging GKE Sandbox. It serves as a robust alternative to local or standard containerized executors by leveraging the **GKE Sandbox** environment, which uses gVisor for workload isolation.

For each code execution request, it dynamically creates an ephemeral Kubernetes Job with a hardened Pod configuration, offering significant security benefits and ensuring that each code execution runs in a clean, isolated environment.

### Key Features of GkeCodeExecutor

* **Dynamic Job Creation**: Uses the Kubernetes `batch/v1` API to create a new Job for each code snippet.
* **Secure Code Mounting**: Injects code into the Pod via a temporary `ConfigMap`, which is mounted to a read-only file.
* **gVisor Sandboxing**: Enforces execution within a `gvisor` runtime for kernel-level isolation.
* **Hardened Security Context**: Pods run as non-root with all Linux capabilities dropped and a read-only root filesystem.
* **Resource Management**: Applies configurable CPU and memory limits to prevent abuse.
* **Automatic Cleanup**: Uses the `ttl_seconds_after_finished` feature on Jobs for robust, automatic garbage collection of completed Pods and Jobs.
* **Node Scheduling**: The executor uses Kubernetes `tolerations` in its Pod specification. This allows the k8s scheduler to place the execution Pod onto a **_pre-configured_** gVisor-enabled node.
* **Module Integration**: The `GkeCodeExecutor` is registered in the `code_executors/__init__.py`, making it available for use by agents. The `ImportError` handling is configured to check for the required `kubernetes` SDK.

### Execution Flow:

1.  Agent invokes `GkeCodeExecutor` with the LLM-generated code.
2.  The `GkeCodeExecutor` will `execute_code` – creates a temporary `ConfigMap`, and then create a k8s `Job` to run it.
3.  This Job runs a standard `python:3.11-slim` container. The image is pulled once to the node and cached. The Job will mount the ConfigMap as `/app/code.py`
4.  The GkeCodeExecutor will monitor the Job to completion, fetch `stdout/stderr` logs from the container, return `CodeExecutionResult` to the LlmAgent, and ensure all temp resources are deleted.
5.  The calling agent formats the result and provides a final response to the user. If the result contains error, it will retry up to `error_retry_attempts` times.

PiperOrigin-RevId: 804511467

contributing/samples/code_execution/gke_sandbox_agent.py

@@ -0,0 +1,49 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""A Python coding agent using the GkeCodeExecutor for secure execution."""
+
+from google.adk.agents import LlmAgent
+from google.adk.code_executors import GkeCodeExecutor
+
+
+def gke_agent_system_instruction():
+  """Returns: The system instruction for the GKE-based coding agent."""
+  return """You are a helpful and capable AI agent that can write and execute Python code to answer questions and perform tasks.
+
+When a user asks a question, follow these steps:
+1.  Analyze the request.
+2.  Write a complete, self-contained Python script to accomplish the task.
+3.  Your code will be executed in a secure, sandboxed environment.
+4.  Return the full and complete output from the code execution, including any text, results, or error messages."""
+
+
+gke_executor = GkeCodeExecutor(
+    # This must match the namespace in your deployment_rbac.yaml where the
+    # agent's ServiceAccount and Role have permissions.
+    namespace="agent-sandbox",
+    # Setting an explicit timeout prevents a stuck job from running forever.
+    timeout_seconds=600,
+)
+
+root_agent = LlmAgent(
+    name="gke_coding_agent",
+    model="gemini-2.0-flash",
+    description=(
+        "A general-purpose agent that executes Python code in a secure GKE"
+        " Sandbox."
+    ),
+    instruction=gke_agent_system_instruction(),
+    code_executor=gke_executor,
+)