From ce2f326eba4cfb78623a9e65b4726fe51b65951f Mon Sep 17 00:00:00 2001
From: heitorlessa <lessa@amazon.co.uk>
Date: Fri, 7 Jul 2023 13:57:52 +0200
Subject: [PATCH] docs(batch): fix custom batch processor example

---
 docs/utilities/batch.md                                   | 2 +-
 examples/batch_processing/src/custom_partial_processor.py | 8 +++-----
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/docs/utilities/batch.md b/docs/utilities/batch.md
index b20598106c5..1b9894a0256 100644
--- a/docs/utilities/batch.md
+++ b/docs/utilities/batch.md
@@ -346,7 +346,7 @@ You can create your own partial batch processor from scratch by inheriting the `
 
 You can then use this class as a context manager, or pass it to `batch_processor` to use as a decorator on your Lambda handler function.
 
-```python hl_lines="9-12 20 35 41 48 59 64 68 76" title="Creating a custom batch processor"
+```python hl_lines="9-11 19 33 39 46 57 62 66 74" title="Creating a custom batch processor"
 --8<-- "examples/batch_processing/src/custom_partial_processor.py"
 ```
 
diff --git a/examples/batch_processing/src/custom_partial_processor.py b/examples/batch_processing/src/custom_partial_processor.py
index 353f612e7cc..f4aaa5733b5 100644
--- a/examples/batch_processing/src/custom_partial_processor.py
+++ b/examples/batch_processing/src/custom_partial_processor.py
@@ -7,8 +7,7 @@
 
 from aws_lambda_powertools import Logger
 from aws_lambda_powertools.utilities.batch import (
-    BasePartialBatchProcessor,
-    EventType,
+    BasePartialProcessor,
     process_partial_response,
 )
 
@@ -17,7 +16,7 @@
 logger = Logger()
 
 
-class MyPartialProcessor(BasePartialBatchProcessor):
+class MyPartialProcessor(BasePartialProcessor):
     """
     Process a record and stores successful results at a Amazon DynamoDB Table
 
@@ -29,8 +28,7 @@ class MyPartialProcessor(BasePartialBatchProcessor):
 
     def __init__(self, table_name: str):
         self.table_name = table_name
-
-        super().__init__(event_type=EventType.SQS)
+        super().__init__()
 
     def _prepare(self):
         # It's called once, *before* processing