diff --git a/3d_segmentation/torch/unet_inference_dict.py b/3d_segmentation/torch/unet_inference_dict.py
new file mode 100644
index 0000000000..84bc902945
--- /dev/null
+++ b/3d_segmentation/torch/unet_inference_dict.py
@@ -0,0 +1,97 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import sys
+import tempfile
+from glob import glob
+
+import nibabel as nib
+import numpy as np
+import torch
+
+from monai.config import print_config
+from monai.data import Dataset, DataLoader, create_test_image_3d
+from monai.inferers import sliding_window_inference
+from monai.networks.nets import UNet
+from monai.transforms import (
+    Activationsd,
+    AsDiscreted,
+    Compose,
+    EnsureChannelFirstd,
+    Invertd,
+    LoadImaged,
+    Orientationd,
+    Resized,
+    SaveImaged,
+    ScaleIntensityd,
+    ToTensord,
+)
+
+
+def main(tempdir):
+    print_config()
+    logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+    print(f"generating synthetic data to {tempdir} (this may take a while)")
+    for i in range(5):
+        im, _ = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)
+        n = nib.Nifti1Image(im, np.eye(4))
+        nib.save(n, os.path.join(tempdir, f"im{i:d}.nii.gz"))
+
+    images = sorted(glob(os.path.join(tempdir, "im*.nii.gz")))
+    files = [{"img": img} for img in images]
+
+    # define pre transforms
+    pre_transforms = Compose([
+        LoadImaged(keys="img"),
+        EnsureChannelFirstd(keys="img"),
+        Orientationd(keys="img", axcodes="RAS"),
+        Resized(keys="img", spatial_size=(96, 96, 96), mode="trilinear", align_corners=True),
+        ScaleIntensityd(keys="img"),
+        ToTensord(keys="img"),
+    ])
+    # define dataset and dataloader
+    dataset = Dataset(data=files, transform=pre_transforms)
+    dataloader = DataLoader(dataset, batch_size=2, num_workers=4)
+    # define post transforms
+    post_transforms = Compose([
+        Activationsd(keys="pred", sigmoid=True),
+        AsDiscreted(keys="pred", threshold_values=True),
+        Invertd(keys="pred", transform=pre_transforms, loader=dataloader, orig_keys="img", nearest_interp=True),
+        SaveImaged(keys="pred_inverted", output_dir="./output", output_postfix="seg", resample=False),
+    ])
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    net = UNet(
+        dimensions=3,
+        in_channels=1,
+        out_channels=1,
+        channels=(16, 32, 64, 128, 256),
+        strides=(2, 2, 2, 2),
+        num_res_units=2,
+    ).to(device)
+    net.load_state_dict(torch.load("best_metric_model_segmentation3d_dict.pth"))
+
+    net.eval()
+    with torch.no_grad():
+        for d in dataloader:
+            images = d["img"].to(device)
+            # define sliding window size and batch size for windows inference
+            d["pred"] = sliding_window_inference(inputs=images, roi_size=(96, 96, 96), sw_batch_size=4, predictor=net)
+            # execute post transforms to invert spatial transforms and save to NIfTI files
+            post_transforms(d)
+
+
+if __name__ == "__main__":
+    with tempfile.TemporaryDirectory() as tempdir:
+        main(tempdir)
diff --git a/modules/inverse_transforms_and_test_time_augmentations.ipynb b/modules/inverse_transforms_and_test_time_augmentations.ipynb
index a1066433a0..6c3debbd37 100644
--- a/modules/inverse_transforms_and_test_time_augmentations.ipynb
+++ b/modules/inverse_transforms_and_test_time_augmentations.ipynb
@@ -114,7 +114,6 @@
     "import monai\n",
     "from monai.apps import download_and_extract\n",
     "from monai.data import (\n",
-    "    BatchInverseTransform,\n",
     "    CacheDataset,\n",
     "    DataLoader,\n",
     "    Dataset,\n",
@@ -141,6 +140,7 @@
     "    RandAffined,\n",
     "    ScaleIntensityd,\n",
     "    ToTensord,\n",
+    "    BatchInverseTransform,\n",
     ")\n",
     "from monai.transforms.utils import allow_missing_keys_mode\n",
     "from monai.utils import first, set_determinism\n",
@@ -1781,7 +1781,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/runexamples.sh b/runexamples.sh
index 7035a79b5a..86810afe2f 100755
--- a/runexamples.sh
+++ b/runexamples.sh
@@ -107,6 +107,12 @@ do
     python "$file"
 done
 
+# run inference files in 3d_segmentation/torch
+for file in "3d_segmentation/torch"/*inference*
+do
+    python "$file"
+done
+
 
 # run training files in 3d_segmentation/ignite
 for file in "3d_segmentation/ignite"/*train*