chore: import upstream snapshot with attribution

contributing/samples/workflows/dynamic_nodes/README.md

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+# ADK Workflow Dynamic Node Execution Sample
+
+## Overview
+
+This sample demonstrates how to use `ctx.run_node` to execute nodes dynamically during workflow execution in **ADK Workflows**.
+
+In standard workflow execution, the execution path is defined statically by the `edges`. However, there are scenarios where the exact nodes, or the number of times a node runs, cannot be determined until runtime.
+
+In this sample, we handle the dynamic loop scenario: an `orchestrate` Python node acts as the driver. It uses a `while True:` loop to first execute a `generate_headline` agent to create a headline based on a given topic, and then an `evaluate_headline` agent to grade it. If the grade is `"tech-related"`, the loop returns the headline. If `"unrelated"`, the feedback is passed back into the state, and the loop repeats.
+
+This is a rewritten version of the standard `loop` sample, achieved without complex graph edge routing (e.g., without conditional routing functions in `edges`), by instead leveraging native Python control flow (`while` loops) combined with asynchronous `ctx.run_node` calls.
+
+## Sample Inputs
+
+- `flower`
+
+- `quantum mechanics`
+
+- `renewable energy`
+
+## Graph
+
+```mermaid
+graph TD
+    START --> orchestrate[orchestrate <br/>PYTHON FUNCTION]
+    orchestrate -.->|ctx.run_node| generate_headline
+    generate_headline --> evaluate_headline
+    evaluate_headline -.-> orchestrate
+```
+
+## How To
+
+1. **Enable Resumability**: For a python node to use `ctx.run_node`, it must be declared with `@node(rerun_on_resume=True)`. This tells the engine to pause and possibly re-run the orchestrator if any dynamically scheduled node gets interrupted (e.g., waiting for human-in-the-loop).
+
+   ```python
+   from google.adk.workflow import node
+
+   @node(rerun_on_resume=True)
+   async def orchestrate(ctx: Context, node_input: str) -> str:
+       # ...
+   ```
+
+1. **Run Node from Context**: Inject `ctx: Context` into your python node definition and await `ctx.run_node(node_to_run)`. The return value is the final output of that execution. You can also yield events to update the state within the loop before the next iteration.
+
+   ```python
+   @node(rerun_on_resume=True)
+   async def orchestrate(ctx: Context, node_input: str) -> str:
+       yield Event(state={"topic": node_input})
+
+       while True:
+           headline = await ctx.run_node(generate_headline)
+           feedback = Feedback.model_validate(
+               await ctx.run_node(evaluate_headline, node_input=headline)
+           )
+
+           if feedback.grade == "tech-related":
+               yield headline
+               break # or return headline
+   ```