chore: import upstream snapshot with attribution

contributing/samples/hitl/request_input_tool/README.md

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+# Request Input Tool Sample
+
+## Overview
+
+This sample demonstrates how an LLM agent can proactively request clarification or confirmation from the user using the built-in `request_input` tool without losing its context/flow.
+
+It showcases a highly realistic support assistant that dynamically constructs a JSON schema to only ask for missing details when creating IT support tickets.
+
+## Sample Inputs
+
+- `I want to file a technical ticket for a database crash.`
+
+  The agent will analyze the prompt, identify that the `title` and `category` are already provided, and dynamically call `request_input` with a schema requesting only `description` and `priority`.
+
+- `File a priority HIGH technical ticket titled database crash explained as the MySQL server throwing OOM errors.`
+
+  The agent has all required details and will call `create_support_ticket` immediately without needing clarification.
+
+## Graph
+
+```mermaid
+graph TD
+    User[User Prompt] --> Agent[Support Assistant Agent]
+    Agent -->|Needs Clarification| RequestInput[request_input tool]
+    RequestInput -->|User Response| Agent
+    Agent -->|All Details Gathered| CreateTicket[create_support_ticket tool]
+```
+
+## How To
+
+This sample uses **Pattern B: Standalone Agents** with the `request_input` tool:
+
+1. **Import `request_input`**:
+
+   ```python
+   from google.adk.tools import request_input
+   ```
+
+1. **Add it to the LLM Agent's tools list**:
+
+   ```python
+   root_agent = Agent(
+       name="support_assistant_agent",
+       tools=[create_support_ticket, request_input],
+       ...
+   )
+   ```
+
+When the LLM decides it needs clarification, it calls `request_input` with a question and a dynamic `response_schema`. The ADK framework automatically intercepts this, yields a long-running interrupt to the client, and injects the user's reply back as a `FunctionResponse` into the LLM's chat history.