chore: import upstream snapshot with attribution

contributing/samples/workflows/parallel_worker/README.md

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+# ADK Workflow Parallel Worker Sample
+
+## Overview
+
+This sample demonstrates how to use **parallel workers** in ADK Workflows.
+
+It takes a user-provided topic, uses an agent to find a list of related topics. The workflow engine will automatically fan-out execution across multiple concurrently running nodes when given an iterable of inputs. First, it dynamically spins up multiple instances of the `make_upper_case` function in parallel to capitalize the topics. Then, it dynamically spins up parallel instances of the `explain_topic` agent to explain each related topic concurrently. Finally, an `aggregate` function collects and formats all the parallel explanations into a single response.
+
+## Sample Inputs
+
+- `machine learning`
+
+- `renewable energy`
+
+- `space exploration`
+
+## Graph
+
+```mermaid
+graph TD
+    START --> process_input
+    process_input --> find_related_topics
+    find_related_topics --> make_upper_case[make_upper_case <br/>parallel_worker=True]
+
+    make_upper_case --> worker1[worker 1]
+    make_upper_case --> worker2[worker 2]
+    make_upper_case --> workerN[worker N]
+
+    worker1 --> explain_topic[explain_topic <br/>parallel_worker=True]
+    worker2 --> explain_topic
+    workerN --> explain_topic
+
+    explain_topic --> eworker1[worker 1]
+    explain_topic --> eworker2[worker 2]
+    explain_topic --> eworkerN[worker N]
+
+    eworker1 --> aggregate
+    eworker2 --> aggregate
+    eworkerN --> aggregate
+```
+
+## How To
+
+Both agents and functions can be designed as parallel workers in an ADK Workflow.
+
+1. Ensure the preceding node in the workflow outputs an iterable (e.g., a `list`). The workflow engine will automatically fan-out and execute the parallel worker node concurrently for each item in the iterable.
+
+1. To define an **Agent** as a parallel worker, use the `parallel_worker=True` parameter:
+
+   ```python
+   explain_topic = Agent(
+       name="explain_topic",
+       instruction="""Explain how the following topic relates to the original topic: "{topic}".""",
+       parallel_worker=True,
+       output_schema=TopicExplanation,
+   )
+   ```
+
+1. To define a **Python function** as a parallel worker, decorate it with `@node(parallel_worker=True)`:
+
+   ```python
+   from google.adk.workflow import node
+
+   @node(parallel_worker=True)
+   def make_upper_case(node_input: str):
+     yield node_input.upper()
+   ```
+
+1. The subsequent node in the workflow will receive the results from all parallel executions as a single aggregated list (e.g., `list[TopicExplanation]`).