change python code block

website/www/site/content/en/blog/deploy-python-pipeline-on-flink-runner.md

@@ -270,7 +270,7 @@ The application first reads text messages from an input Kafka topic. Next, it ex
 
 We create a custom *Java IO Expansion Service* (`get_expansion_service`) and add it to the `ReadFromKafka` and `WriteToKafka` transforms of the Kafka Connector I/O. Although the Kafka I/O provides a function to create that service, it did not work for me (or I do not understand how to make use of it yet). Instead, I created a custom service, as illustrated in [Building Big Data Pipelines with Apache Beam by Jan Lukavský](https://www.packtpub.com/product/building-big-data-pipelines-with-apache-beam/9781800564930). The expansion service Jar file (`beam-sdks-java-io-expansion-service.jar`) must exist in the Kubernetes [*job*](https://kubernetes.io/docs/concepts/workloads/controllers/job/) that executes the pipeline, while the Java SDK (`/opt/apache/beam/boot`) must exist in the runner worker.
 
-{{< highlight py >}}
+```python
 # beam/word_len/word_len.py
 import json
 import argparse
@@ -500,16 +500,16 @@ def run(argv=None, save_main_session=True):
 
 if __name__ == "__main__":
     run()
-{{< /highlight >}}
+```
 
 The pipeline script is added to a Python package under a folder named `word_len`. A simple module named `run` is created, because it is executed as a module, for example, `python -m ...`. When I ran the pipeline as a script, I encountered an error. This packaging method is for demonstration only. For a recommended way of packaging a pipeline, see [Managing Python Pipeline Dependencies](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/).
 
-{{< highlight py >}}
+```python
 # beam/word_len/run.py
 from . import *
 
 run()
-{{< /highlight >}}
+```
 
 Overall, the pipeline package uses the following structure.
 
@@ -759,7 +759,7 @@ alt="Kafka Input Topic">
 
 A simple Python Kafka producer is created to check the output of the application. By default, the producer app sends random text from the [Faker](https://faker.readthedocs.io/en/master/) package to the input Kafka topic every one second.
 
-{{< highlight py >}}
+```python
 # kafka/client/producer.py
 import os
 import time
@@ -812,7 +812,7 @@ if __name__ == "__main__":
         if num_events % 5 == 0:
             print(f"<<<<<{num_events} text sent... current>>>>\n{text}")
         time.sleep(int(os.getenv("DELAY_SECONDS", "1")))
-{{< /highlight >}}
+```
 
 Expose the Kafka bootstrap server on port 29092 using the `kubectl port-forward` command. Execute the Python script to start the producer app.