From 523875cea7f40a38835be153f3252775d362ace7 Mon Sep 17 00:00:00 2001
From: Johannes Messner <messnerjo@gmail.com>
Date: Wed, 29 Mar 2023 14:34:46 +0200
Subject: [PATCH] docs: fix docstring example of find_batched

Signed-off-by: Johannes Messner <messnerjo@gmail.com>
---
 docarray/utils/find.py | 66 +++++++++++++++++++++---------------------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/docarray/utils/find.py b/docarray/utils/find.py
index c7eeb787159..a626134d1b6 100644
--- a/docarray/utils/find.py
+++ b/docarray/utils/find.py
@@ -130,39 +130,39 @@ def find_batched(
     ---
 
     ```python
-    # from docarray import DocArray, BaseDoc
-    # from docarray.typing import TorchTensor
-    # from docarray.utils.find import find
-    # import torch
-    #
-    #
-    # class MyDocument(BaseDoc):
-    #     embedding: TorchTensor
-    #
-    #
-    # index = DocArray[MyDocument](
-    #     [MyDocument(embedding=torch.rand(128)) for _ in range(100)]
-    # )
-    #
-    # # use DocArray as query
-    # query = DocArray[MyDocument]([MyDocument(embedding=torch.rand(128)) for _ in range(3)])
-    # results = find(
-    #     index=index,
-    #     query=query,
-    #     embedding_field='embedding',
-    #     metric='cosine_sim',
-    # )
-    # top_matches, scores = results[0]
-    #
-    # # use tensor as query
-    # query = torch.rand(3, 128)
-    # results, scores = find(
-    #     index=index,
-    #     query=query,
-    #     embedding_field='embedding',
-    #     metric='cosine_sim',
-    # )
-    # top_matches, scores = results[0]
+    from docarray import DocArray, BaseDoc
+    from docarray.typing import TorchTensor
+    from docarray.utils.find import find_batched
+    import torch
+
+
+    class MyDocument(BaseDoc):
+        embedding: TorchTensor
+
+
+    index = DocArray[MyDocument](
+        [MyDocument(embedding=torch.rand(128)) for _ in range(100)]
+    )
+
+    # use DocArray as query
+    query = DocArray[MyDocument]([MyDocument(embedding=torch.rand(128)) for _ in range(3)])
+    results = find_batched(
+        index=index,
+        query=query,
+        embedding_field='embedding',
+        metric='cosine_sim',
+    )
+    top_matches, scores = results[0]
+
+    # use tensor as query
+    query = torch.rand(3, 128)
+    results = find_batched(
+        index=index,
+        query=query,
+        embedding_field='embedding',
+        metric='cosine_sim',
+    )
+    top_matches, scores = results[0]
     ```
 
     ---