diff --git a/docs/Examples/HWF.rst b/docs/Examples/HWF.rst
index fb03964..86dbc6b 100644
--- a/docs/Examples/HWF.rst
+++ b/docs/Examples/HWF.rst
@@ -197,7 +197,7 @@ for images. As shown below:
 
 .. code:: ipython3
 
-    data_instances = [torch.randn(1, 45, 45).to(device) for _ in range(32)]
+    data_instances = [torch.randn(1, 45, 45) for _ in range(32)]
     pred_idx = base_model.predict(X=data_instances)
     print(f"Predicted class index for a batch of 32 instances: " +
           f"{type(pred_idx).__name__} with shape {pred_idx.shape}")
diff --git a/docs/Examples/MNISTAdd.rst b/docs/Examples/MNISTAdd.rst
index cd5f786..bedb86a 100644
--- a/docs/Examples/MNISTAdd.rst
+++ b/docs/Examples/MNISTAdd.rst
@@ -169,7 +169,7 @@ for images. As shown below:
 
 .. code:: ipython3
 
-    data_instances = [torch.randn(1, 28, 28).to(device) for _ in range(32)]
+    data_instances = [torch.randn(1, 28, 28) for _ in range(32)]
     pred_idx = base_model.predict(X=data_instances)
     print(f"Predicted class index for a batch of 32 instances: np.ndarray with shape {pred_idx.shape}")
     pred_prob = base_model.predict_proba(X=data_instances)
diff --git a/examples/hwf/hwf.ipynb b/examples/hwf/hwf.ipynb
index 3e9b4d5..5967b21 100644
--- a/examples/hwf/hwf.ipynb
+++ b/examples/hwf/hwf.ipynb
@@ -193,7 +193,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_instances = [torch.randn(1, 45, 45).to(device) for _ in range(32)]\n",
+    "data_instances = [torch.randn(1, 45, 45) for _ in range(32)]\n",
     "pred_idx = base_model.predict(X=data_instances)\n",
     "print(\n",
     "    f\"Predicted class index for a batch of 32 instances: \"\n",
@@ -440,7 +440,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.8.13"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/examples/mnist_add/mnist_add.ipynb b/examples/mnist_add/mnist_add.ipynb
index 4202c99..67aee51 100644
--- a/examples/mnist_add/mnist_add.ipynb
+++ b/examples/mnist_add/mnist_add.ipynb
@@ -226,7 +226,7 @@
     }
    ],
    "source": [
-    "data_instances = [torch.randn(1, 28, 28).to(device) for _ in range(32)]\n",
+    "data_instances = [torch.randn(1, 28, 28) for _ in range(32)]\n",
     "pred_idx = base_model.predict(X=data_instances)\n",
     "print(\n",
     "    f\"Predicted class index for a batch of 32 instances: \"\n",