[FIX] minor fix

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}")

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)

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": {

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",