diff --git a/doc/source/data/batch_inference.rst b/doc/source/data/batch_inference.rst
index 6759daab3ae1..c26eba992203 100644
--- a/doc/source/data/batch_inference.rst
+++ b/doc/source/data/batch_inference.rst
@@ -5,7 +5,7 @@ End-to-end: Offline Batch Inference
 
 .. tip::
 
-    `Get in touch <https://forms.gle/sGX7PQhheBGL6yxQ6>`_ to get help using Ray Data, the industry's fastest and cheapest solution for offline batch inference. 
+    `Get in touch <https://forms.gle/sGX7PQhheBGL6yxQ6>`_ to get help using Ray Data, the industry's fastest and cheapest solution for offline batch inference.
 
 Offline batch inference is a process for generating model predictions on a fixed set of input data. Ray Data offers an efficient and scalable solution for batch inference, providing faster execution and cost-effectiveness for deep learning applications.
 
@@ -27,7 +27,7 @@ To start, install Ray Data:
 Using Ray Data for offline inference involves four basic steps:
 
 - **Step 1:** Load your data into a Ray Dataset. Ray Data supports many different data sources and formats. For more details, see :ref:`Loading Data <loading_data>`.
-- **Step 2:** Define a Python class to load the pre-trained model. 
+- **Step 2:** Define a Python class to load the pre-trained model.
 - **Step 3:** Transform your dataset using the pre-trained model by calling :meth:`ds.map_batches() <ray.data.Dataset.map_batches>`. For more details, see :ref:`Transforming Data <transforming_data>`.
 - **Step 4:** Get the final predictions by either iterating through the output or saving the results. For more details, see the :ref:`Iterating over data <iterating-over-data>` and :ref:`Saving data <saving-data>` user guides.
 
@@ -37,14 +37,14 @@ For how to configure batch inference, see :ref:`the configuration guide<batch_in
 .. tabs::
 
     .. group-tab:: HuggingFace
-        
+
         .. testcode::
-            
+
             from typing import Dict
             import numpy as np
 
             import ray
-            
+
             # Step 1: Create a Ray Dataset from in-memory Numpy arrays.
             # You can also create a Ray Dataset from many other sources and file
             # formats.
@@ -77,12 +77,12 @@ For how to configure batch inference, see :ref:`the configuration guide<batch_in
             predictions = ds.map_batches(HuggingFacePredictor, compute=scale)
             # Step 4: Show one prediction output.
             predictions.show(limit=1)
-        
+
         .. testoutput::
-            :skipif: True
+            :options: +MOCK
 
             {'data': 'Complete this', 'output': 'Complete this information or purchase any item from this site.\n\nAll purchases are final and non-'}
-        
+
 
     .. group-tab:: PyTorch
 
@@ -129,7 +129,7 @@ For how to configure batch inference, see :ref:`the configuration guide<batch_in
             predictions.show(limit=1)
 
         .. testoutput::
-            :skipif: True
+            :options: +MOCK
 
             {'output': array([0.5590901], dtype=float32)}
 
@@ -141,7 +141,7 @@ For how to configure batch inference, see :ref:`the configuration guide<batch_in
             import numpy as np
 
             import ray
-            
+
             # Step 1: Create a Ray Dataset from in-memory Numpy arrays.
             # You can also create a Ray Dataset from many other sources and file
             # formats.
@@ -174,7 +174,7 @@ For how to configure batch inference, see :ref:`the configuration guide<batch_in
             predictions.show(limit=1)
 
         .. testoutput::
-            :skipif: True
+            :options: +MOCK
 
             {'output': array([0.625576], dtype=float32)}
 
@@ -182,7 +182,7 @@ For how to configure batch inference, see :ref:`the configuration guide<batch_in
 
 More examples
 -------------
-- :doc:`Image Classification Batch Inference with PyTorch ResNet18 </data/examples/pytorch_resnet_batch_prediction>` 
+- :doc:`Image Classification Batch Inference with PyTorch ResNet18 </data/examples/pytorch_resnet_batch_prediction>`
 - :doc:`Object Detection Batch Inference with PyTorch FasterRCNN_ResNet50 </data/examples/batch_inference_object_detection>`
 - :doc:`Image Classification Batch Inference with Huggingface Vision Transformer </data/examples/huggingface_vit_batch_prediction>`
 
@@ -200,21 +200,21 @@ To use GPUs for inference, make the following changes to your code:
 
 1. Update the class implementation to move the model and data to and from GPU.
 2. Specify `num_gpus=1` in the :meth:`ds.map_batches() <ray.data.Dataset.map_batches>` call to indicate that each actor should use 1 GPU.
-3. Specify a `batch_size` for inference. For more details on how to configure the batch size, see `batch_inference_batch_size`_. 
+3. Specify a `batch_size` for inference. For more details on how to configure the batch size, see `batch_inference_batch_size`_.
 
 The remaining is the same as the :ref:`Quickstart <batch_inference_quickstart>`.
 
 .. tabs::
 
     .. group-tab:: HuggingFace
-        
+
         .. testcode::
-            
+
             from typing import Dict
             import numpy as np
 
             import ray
-            
+
             ds = ray.data.from_numpy(np.asarray(["Complete this", "for me"]))
 
             class HuggingFacePredictor:
@@ -230,21 +230,21 @@ The remaining is the same as the :ref:`Quickstart <batch_inference_quickstart>`.
 
             # Use 2 actors, each actor using 1 GPU. 2 GPUs total.
             predictions = ds.map_batches(
-                HuggingFacePredictor, 
+                HuggingFacePredictor,
                 num_gpus=1,
-                # Specify the batch size for inference. 
+                # Specify the batch size for inference.
                 # Increase this for larger datasets.
-                batch_size=1, 
+                batch_size=1,
                 # Set the ActorPool size to the number of GPUs in your cluster.
-                compute=ray.data.ActorPoolStrategy(size=2), 
+                compute=ray.data.ActorPoolStrategy(size=2),
                 )
             predictions.show(limit=1)
-        
+
         .. testoutput::
-            :skipif: True
+            :options: +MOCK
 
             {'data': 'Complete this', 'output': 'Complete this poll. Which one do you think holds the most promise for you?\n\nThank you'}
-        
+
 
     .. group-tab:: PyTorch
 
@@ -277,18 +277,18 @@ The remaining is the same as the :ref:`Quickstart <batch_inference_quickstart>`.
 
             # Use 2 actors, each actor using 1 GPU. 2 GPUs total.
             predictions = ds.map_batches(
-                TorchPredictor, 
+                TorchPredictor,
                 num_gpus=1,
-                # Specify the batch size for inference. 
+                # Specify the batch size for inference.
                 # Increase this for larger datasets.
                 batch_size=1,
                 # Set the ActorPool size to the number of GPUs in your cluster.
-                compute=ray.data.ActorPoolStrategy(size=2) 
+                compute=ray.data.ActorPoolStrategy(size=2)
                 )
             predictions.show(limit=1)
 
         .. testoutput::
-            :skipif: True
+            :options: +MOCK
 
             {'output': array([0.5590901], dtype=float32)}
 
@@ -302,7 +302,7 @@ The remaining is the same as the :ref:`Quickstart <batch_inference_quickstart>`.
             from tensorflow import keras
 
             import ray
-            
+
             ds = ray.data.from_numpy(np.ones((1, 100)))
 
             class TFPredictor:
@@ -320,18 +320,18 @@ The remaining is the same as the :ref:`Quickstart <batch_inference_quickstart>`.
 
             # Use 2 actors, each actor using 1 GPU. 2 GPUs total.
             predictions = ds.map_batches(
-                TFPredictor, 
+                TFPredictor,
                 num_gpus=1,
-                # Specify the batch size for inference. 
+                # Specify the batch size for inference.
                 # Increase this for larger datasets.
                 batch_size=1,
                 # Set the ActorPool size to the number of GPUs in your cluster.
-                compute=ray.data.ActorPoolStrategy(size=2) 
+                compute=ray.data.ActorPoolStrategy(size=2)
                 )
             predictions.show(limit=1)
 
         .. testoutput::
-            :skipif: True
+            :options: +MOCK
 
             {'output': array([0.625576], dtype=float32)}
 
@@ -345,7 +345,7 @@ Configure the size of the input batch that is passed to ``__call__`` by setting
 Increasing batch size results in faster execution because inference is a vectorized operation. For GPU inference, increasing batch size increases GPU utilization. Set the batch size to as large possible without running out of memory. If you encounter OOMs, decreasing ``batch_size`` may help.
 
 .. testcode::
-    
+
     import numpy as np
 
     import ray
@@ -355,7 +355,7 @@ Increasing batch size results in faster execution because inference is a vectori
     def assert_batch(batch: Dict[str, np.ndarray]):
         assert len(batch) == 2
         return batch
-    
+
     # Specify that each input batch should be of size 2.
     ds.map_batches(assert_batch, batch_size=2)
 
@@ -392,12 +392,12 @@ Suppose your cluster has 4 nodes, each with 16 CPUs. To limit to at most
 
 .. testcode::
     :skipif: True
-            
+
     from typing import Dict
     import numpy as np
 
     import ray
-    
+
     ds = ray.data.from_numpy(np.asarray(["Complete this", "for me"]))
 
     class HuggingFacePredictor:
@@ -411,10 +411,10 @@ Suppose your cluster has 4 nodes, each with 16 CPUs. To limit to at most
             return batch
 
     predictions = ds.map_batches(
-        HuggingFacePredictor, 
+        HuggingFacePredictor,
         # Require 5 CPUs per actor (so at most 3 can fit per 16 CPU node).
         num_cpus=5,
         # 3 actors per node, with 4 nodes in the cluster means ActorPool size of 12.
-        compute=ray.data.ActorPoolStrategy(size=12) 
+        compute=ray.data.ActorPoolStrategy(size=12)
         )
     predictions.show(limit=1)