Fix code sample alignment & trim trailing whitespace (#1038)

Author: dandavison

README.md

@@ -1489,21 +1489,21 @@ https://github.com/temporalio/samples-python/tree/nexus/hello_nexus).
 
 ### Plugins
 
-Plugins provide a way to extend and customize the behavior of Temporal clients and workers through a chain of 
-responsibility pattern. They allow you to intercept and modify client creation, service connections, worker 
+Plugins provide a way to extend and customize the behavior of Temporal clients and workers through a chain of
+responsibility pattern. They allow you to intercept and modify client creation, service connections, worker
 configuration, and worker execution. Common customizations may include but are not limited to:
 
 1. DataConverter
 2. Activities
 3. Workflows
 4. Interceptors
 
-A single plugin class can implement both client and worker plugin interfaces to share common logic between both 
+A single plugin class can implement both client and worker plugin interfaces to share common logic between both
 contexts. When used with a client, it will automatically be propagated to any workers created with that client.
 
 #### Client Plugins
 
-Client plugins can intercept and modify client configuration and service connections. They are useful for adding 
+Client plugins can intercept and modify client configuration and service connections. They are useful for adding
 authentication, modifying connection parameters, or adding custom behavior during client creation.
 
 Here's an example of a client plugin that adds custom authentication:
@@ -1515,7 +1515,7 @@ import temporalio.service
 class AuthenticationPlugin(Plugin):
     def __init__(self, api_key: str):
         self.api_key = api_key
-        
+
     def init_client_plugin(self, next: Plugin) -> None:
       self.next_client_plugin = next
 
@@ -1540,10 +1540,10 @@ client = await Client.connect(
 
 #### Worker Plugins
 
-Worker plugins can modify worker configuration and intercept worker execution. They are useful for adding monitoring, 
-custom lifecycle management, or modifying worker settings. Worker plugins can also configure replay. 
-They should do this in the case that they modified the worker in a way which would also need to be present 
-for replay to function. For instance, changing the data converter or adding workflows. 
+Worker plugins can modify worker configuration and intercept worker execution. They are useful for adding monitoring,
+custom lifecycle management, or modifying worker settings. Worker plugins can also configure replay.
+They should do this in the case that they modified the worker in a way which would also need to be present
+for replay to function. For instance, changing the data converter or adding workflows.
 
 Here's an example of a worker plugin that adds custom monitoring:
 
@@ -1560,7 +1560,7 @@ class MonitoringPlugin(Plugin):
 
     def init_worker_plugin(self, next: Plugin) -> None:
         self.next_worker_plugin = next
-        
+
     def configure_worker(self, config: WorkerConfig) -> WorkerConfig:
         # Modify worker configuration
         original_task_queue = config["task_queue"]
@@ -1574,22 +1574,22 @@ class MonitoringPlugin(Plugin):
             await self.next_worker_plugin.run_worker(worker)
         finally:
             self.logger.info("Worker execution completed")
-      
-  def configure_replayer(self, config: ReplayerConfig) -> ReplayerConfig:
-      return self.next_worker_plugin.configure_replayer(config)
-  
-  @asynccontextmanager
-  async def run_replayer(
-      self,
-      replayer: Replayer,
-      histories: AsyncIterator[temporalio.client.WorkflowHistory],
-  ) -> AsyncIterator[AsyncIterator[WorkflowReplayResult]]:
-        self.logger.info("Starting replay execution")
-        try:
-          async with self.next_worker_plugin.run_replayer(replayer, histories) as results:
-              yield results
-        finally:
-            self.logger.info("Replay execution completed")
+
+    def configure_replayer(self, config: ReplayerConfig) -> ReplayerConfig:
+        return self.next_worker_plugin.configure_replayer(config)
+
+    @asynccontextmanager
+    async def run_replayer(
+        self,
+        replayer: Replayer,
+        histories: AsyncIterator[temporalio.client.WorkflowHistory],
+    ) -> AsyncIterator[AsyncIterator[WorkflowReplayResult]]:
+          self.logger.info("Starting replay execution")
+          try:
+            async with self.next_worker_plugin.run_replayer(replayer, histories) as results:
+                yield results
+          finally:
+              self.logger.info("Replay execution completed")
 
 # Use the plugin when creating a worker
 worker = Worker(
@@ -1617,38 +1617,38 @@ class UnifiedPlugin(ClientPlugin, WorkerPlugin):
 
     def init_worker_plugin(self, next: WorkerPlugin) -> None:
         self.next_worker_plugin = next
-  
+
     def configure_client(self, config: ClientConfig) -> ClientConfig:
         # Client-side customization
         config["data_converter"] = pydantic_data_converter
         return self.next_client_plugin.configure_client(config)
-  
+
     async def connect_service_client(
         self, config: temporalio.service.ConnectConfig
     ) -> temporalio.service.ServiceClient:
         # Add authentication to the connection
         config.api_key = self.api_key
         return await self.next_client_plugin.connect_service_client(config)
-        
+
     def configure_worker(self, config: WorkerConfig) -> WorkerConfig:
         # Worker-side customization
         return self.next_worker_plugin.configure_worker(config)
-  
+
     async def run_worker(self, worker: Worker) -> None:
         print("Starting unified worker")
         await self.next_worker_plugin.run_worker(worker)
-        
+
     def configure_replayer(self, config: ReplayerConfig) -> ReplayerConfig:
         config["data_converter"] = pydantic_data_converter
         return self.next_worker_plugin.configure_replayer(config)
-    
-    def run_replayer(
+
+    async def run_replayer(
         self,
         replayer: Replayer,
         histories: AsyncIterator[temporalio.client.WorkflowHistory],
     ) -> AbstractAsyncContextManager[AsyncIterator[WorkflowReplayResult]]:
         return self.next_worker_plugin.run_replayer(replayer, histories)
-              
+
 # Create client with the unified plugin
 client = await Client.connect(
     "localhost:7233",