chore: import upstream snapshot with attribution

contributing/samples/workflows/request_input_advanced/README.md

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+# ADK Workflow Request Input Advanced Sample
+
+## Overview
+
+This sample demonstrates advanced features for requesting Human-in-the-Loop (HITL) input dynamically during an **ADK Workflow** execution.
+
+Specifically, it highlights how to pass structured data to the client UI using the `payload` parameter, and how to mandate a structured response type using the `response_schema` parameter on the yielded `RequestInput` event.
+
+In this scenario, an employee requests time off by providing a natural language description of their request (e.g., "I need next Monday off to go to the dentist").
+
+- An LLM agent (`process_request`) parses the natural language into a structured Pydantic model containing the number of `days` and a `reason`.
+- A python node (`evaluate_request`) evaluates the parsed request:
+  - If `days <= 1`, it yields a `TimeOffDecision` approving the request.
+  - If `days > 1`, it yields a `RequestInput` to a manager. It attaches the request details to the `payload` so the client UI can render it. It enforces that the manager must respond with a JSON object containing an `approved` boolean and an optional `approved_days` integer by specifying `response_schema` with a valid Pydantic JSON schema.
+
+## Sample Inputs
+
+Start the workflow by providing the initial time off request in natural language:
+
+- `I'm feeling under the weather and need to take today off.`
+
+  *Parses as 1 day, auto-approves.*
+
+- `Taking my family to Disney World, I'll be out for 5 days next week.`
+
+  *Parses as 5 days, routes to manager review.*
+
+When the terminal prompts you as the manager, provide valid JSON matching the schema:
+
+- `{"approved": true, "approved_days": 5}`
+
+- `{"approved": false, "approved_days": 0}`
+
+## Graph
+
+```mermaid
+graph TD
+    START --> process_request[process_request <br/>LLM Agent]
+    process_request --> evaluate_request
+    evaluate_request -->|Yields TimeOffDecision OR RequestInput| process_decision
+    process_decision --> END[END]
+```
+
+## How To
+
+1. **Define the Response Schema:** Use a Pydantic model's `model_json_schema()` to get a standard layout of what the human should return.
+
+   ```python
+   from typing import Optional
+   from pydantic import BaseModel, Field
+
+   class TimeOffDecision(BaseModel):
+       approved: bool = Field(...)
+       approved_days: Optional[int] = Field(None)
+   ```
+
+1. **Yield a RequestInput:** Pass the schema and optionally a `payload` for the client to display.
+
+   ```python
+   def evaluate_request(request: TimeOffRequest):
+       # ... logic to check if manager review is needed ...
+       yield RequestInput(
+           interrupt_id="manager_approval",
+           message="Please review this time off request.",
+           payload=request,
+           response_schema=TimeOffDecision.model_json_schema()
+       )
+   ```
+
+1. **Parse the Resumed Input:** When the workflow resumes, the `node_input` to the next node will be the parsed Pydantic model implicitly (if type-hinted).
+
+   ```python
+   def process_decision(request: TimeOffRequest, node_input: TimeOffDecision):
+       if node_input.approved:
+           # ...
+   ```