chore: import upstream snapshot with attribution

contributing/samples/workflows/node_output/README.md

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+# ADK Workflow Node Output Sample
+
+## Overview
+
+This sample demonstrates how to manage component outputs and structure data between nodes in an **ADK Workflow**.
+
+When stringing nodes together, it's critical to know how the ADK framework passes data along edges. This sample shows:
+
+1. Returning a raw string (it gets automatically wrapped in an `Event`).
+1. Returning an explicit `Event` for more granular control over routes and state.
+1. Generating a structured dictionary via `Agent(output_schema=MyModel)`.
+1. Automatically coercing that raw dictionary back into a fully formed Pydantic model simply by defining it as a type-hint parameter in the Python function.
+
+## Sample Inputs
+
+- `cyberpunk future`
+
+- `gardening tips for beginners`
+
+## Graph
+
+```mermaid
+graph TD
+    START --> generate_string_output
+    generate_string_output --> generate_event_output
+    generate_event_output --> generate_pydantic_output
+    generate_pydantic_output --> consume_pydantic_output
+```
+
+## How To
+
+1. **Return raw types (string, dict, list):** The node runner will automatically wrap primitives in an `Event(output=...)`.
+
+   ```python
+   def generate_string_output(node_input: str):
+       return "Processed input: " + node_input
+   ```
+
+1. **Return an Event explicitly:** Use this when you also need to emit a `route` or modify `ctx.state`.
+
+   ```python
+   def generate_event_output(node_input: str):
+       return Event(output=f"Wrapped output: {node_input}")
+   ```
+
+1. **Generate structured data from an LLM:** Pass a Pydantic class to the `Agent`'s `output_schema`. The LLM returns a dictionary/JSON matching the structure.
+
+   ```python
+   class TopicDetails(BaseModel):
+       title: str
+       description: str
+       category: str
+
+   generate_pydantic_output = Agent(
+       name="generate_pydantic_output",
+       output_schema=TopicDetails,
+   )
+   ```
+
+1. **Consume structured data in a function:** Simply type-hint the parameter. `FunctionNode` leverages Pydantic to parse the dictionary back into your fully accessible `TopicDetails` class automatically before your function starts running.
+
+   ```python
+   def consume_pydantic_output(node_input: TopicDetails):
+       # Type coercion converts dict to model. Now you have .title, .category, etc.
+       return f"Title: {node_input.title}"
+   ```