diff --git a/.circleci/config.yml b/.circleci/config.yml
index bd3f3c536c..dcbc84cc9a 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -19,6 +19,81 @@ commands:
             sudo wget -q https://github.com/bazelbuild/bazel/releases/download/<< parameters.version >>/bazel-<< parameters.version >>-linux-<< parameters.platform >> -O /usr/bin/bazel
             sudo chmod a+x /usr/bin/bazel
 
+  install-cmake:
+    description: Install CMake.
+    parameters:
+        cache:
+            default: true
+            description: Whether or not to cache the installation.
+            type: boolean
+        cache-key:
+            default: v1
+            description: String to use in cache key. Typically overridden when needed to bust cache.
+            type: string
+        install-dir:
+            default: $HOME/cmake
+            description: The path to install CMake to.
+            type: string
+        version:
+            default: 3.18.2
+            description: The version of CMake to install.
+            type: string
+    steps:
+        - when:
+            condition:
+                equal:
+                    - << parameters.cache >>
+                    - true
+            steps:
+                - run:
+                    command: mkdir -pv << parameters.install-dir >>
+                    name: Prep cache restore
+                - restore_cache:
+                    keys:
+                        - cmake-<< parameters.cache-key >>-<< parameters.version >>
+        - run:
+            command: |
+                echo 'export PATH="<< parameters.install-dir >>/bin:$PATH"' >> $BASH_ENV
+            name: Add CMake to PATH
+        - run:
+            command: |
+                if which cmake; then
+                  if cmake --version | grep "<< parameters.version >>"; then
+                    echo "CMake is already installed."
+                    exit 0
+                  else
+                    echo "CMake is already installed but it is the wrong version."
+                  fi
+                fi
+
+                rm -rf << parameters.install-dir >>/*
+                echo "Installing the requested version of CMake."
+                baseUrl="https://github.com/Kitware/CMake/releases/download/"
+                url="${baseUrl}/v<< parameters.version >>/cmake-<< parameters.version >>-Linux-x86_64.tar.gz"
+                curl -sSL -o /tmp/cmake.tar.gz $url
+                tar -C << parameters.install-dir >> --strip-components 1 -zxf /tmp/cmake.tar.gz
+            name: Install CMake
+        - run:
+            command: rm -rf << parameters.install-dir >>/{doc,man}
+            name: Remove unnecessary files
+        - run:
+            command: |
+                ls -l << parameters.install-dir >>/bin
+                echo $PATH
+                cmake --version
+            name: Verify CMake installation
+        - when:
+            condition:
+                equal:
+                    - << parameters.cache >>
+                    - true
+            steps:
+                - save_cache:
+                    key: cmake-<< parameters.cache-key >>-<< parameters.version >>
+                    paths:
+                        - << parameters.install-dir >>
+
+
   install-cuda:
     description: "Install CUDA"
     parameters:
@@ -207,6 +282,80 @@ commands:
             mkdir -p /tmp/dist/builds
             cp dist/* /tmp/dist/builds
 
+  build-py-ngc:
+    description: "Build the torch-tensorrt python release for NGC PyTorch (cxx11-abi)"
+    parameters:
+      release:
+        type: boolean
+        default: false
+      torch-base-image:
+        type: string
+        default: nvcr.io/nvidia/pytorch:latest
+    steps:
+      - run:
+          name: Log into docker
+          command: |
+            docker login --username="\$oauthtoken" --password=$NGC_TOKEN nvcr.io
+      - run:
+          name: Pull image
+          command: |
+            docker pull << parameters.torch-base-image >>
+      - run:
+          name: Create container
+          command: |
+              docker run -it -d --name ngc_build_container -v /home/circleci/project:/workspace -e BAZEL_VERSION="$(cat /home/circleci/project/.bazelversion)" -w /workspace << parameters.torch-base-image >>
+      - run:
+          name: Install build deps
+          command: |
+            export BAZEL_VERSION="$(cat /home/circleci/project/.bazelversion)"
+            docker exec ngc_build_container bash -c "wget https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-linux-x86_64 -O /usr/bin/bazel && chmod a+x /usr/bin/bazel"
+            docker exec ngc_build_container bash -c "mv /workspace/docker/WORKSPACE.ngc /workspace/WORKSPACE"
+            docker exec ngc_build_container bash -c "cd /workspace/py && pip install wheel setuptools pybind11==2.6.2"
+            docker exec ngc_build_container bash -c "git config --global --add safe.directory /workspace"
+      - when:
+          condition: << parameters.release >>
+          steps:
+            - run:
+                name: Build torch-tensorrt release build for NGC
+                command: |
+                  docker exec ngc_build_container bash -c "cd /workspace/py && python3 setup.py bdist_wheel --use-cxx11-abi --release"
+      - unless:
+          condition: << parameters.release >>
+          steps:
+            - run:
+                name: Build torch-tensorrt debug build for NGC
+                command: |
+                  docker exec ngc_build_container bash -c "cd /workspace/py && python3 setup.py bdist_wheel --use-cxx11-abi"
+      - run:
+          name: Collect builds
+          command: |
+            mkdir -p /tmp/dist/builds
+            cp /home/circleci/project/py/dist/* /tmp/dist/builds
+
+  build-cmake:
+    description: "Build the torch-tensorrt using CMake"
+    parameters:
+      platform:
+        type: string
+        default: "x86_64"
+    steps:
+      - run:
+          name: Build torch-tensorrt library with CMake
+          command: |
+            mkdir build
+            export PATH=$PATH:/usr/local/cuda/bin
+            ~/cmake/bin/cmake -S. -Bbuild \
+              -DCMAKE_MODULE_PATH=cmake/Module \
+              -DTorch_DIR=/opt/circleci/.pyenv/versions/3.9.4/lib/python3.9/site-packages/torch/share/cmake/Torch \
+              -DTensorRT_ROOT=/usr \
+              -DCMAKE_BUILD_TYPE=Debug
+            cmake --build build -- -j12
+            mkdir -p /tmp/dist/builds
+            cp -r build/bin /tmp/dist/builds
+            cp -r build/lib /tmp/dist/builds
+            cp -r cpp/include /tmp/dist/builds
+
+
   dump-test-env:
     description: "Dump the test env to console"
     steps:
@@ -632,7 +781,7 @@ jobs:
                 platform: "x86_64"
                 cudnn-version: << pipeline.parameters.cudnn-release-version >>
                 trt-version-short:  << pipeline.parameters.trt-release-version-short >>
-                bazel-version: "5.1.1"
+                bazel-version: << pipeline.parameters.bazel-version >>
                 bazel-platform: "x86_64"
             - run:
                 name: Build cxx11-abi tarball
@@ -703,7 +852,7 @@ jobs:
           platform: "sbsa"
           cudnn-version: << pipeline.parameters.cudnn-jetson-version >>
           trt-version-short:  << pipeline.parameters.trt-jetson-version-short >>
-          bazel-version: "5.1.1"
+          bazel-version: << pipeline.parameters.bazel-version >>
           bazel-platform: "arm64"
       - run:
           name: Set python version
@@ -730,7 +879,7 @@ jobs:
             TORCHTRT_VERSION=$(cd torch_tensorrt && python3 -c "from _version import __version__;print(__version__)")
             TRT_VERSION=$(cd torch_tensorrt && python3 -c "from _version import __tensorrt_version__;print(__tensorrt_version__)")
             CUDNN_VERSION=$(cd torch_tensorrt && python3 -c "from _version import __cudnn_version__;print(__cudnn_version__)")
-            pip3 install -r ~/project/py/requirements.txt
+            python3 -m pip install pybind11==2.6.2
             TORCH_VERSION=$(python3 -c "from torch import __version__;print(__version__.split('+')[0])")
             cp ~/project/bazel-bin/libtorchtrt.tar.gz ~/project/py/dist/libtorchtrt-${TORCHTRT_VERSION}-cudnn${CUDNN_VERSION}-tensorrt${TRT_VERSION}-cuda${CUDA_VERSION}-libtorch${TORCH_VERSION}-aarch64-linux-jp<< parameters.jetpack-version >>.tar.gz
       - run:
@@ -742,6 +891,103 @@ jobs:
           path: /tmp/dist/jetson
           destination: aarch64-release-pkgs
 
+  build-x86_64-cmake:
+    parameters:
+      cudnn-version:
+        type: string
+      trt-version-short:
+        type: string
+      torch-build:
+        type: string
+      torch-build-index:
+        type: string
+    machine:
+      image: ubuntu-2004-cuda-11.4:202110-01
+    resource_class: xlarge
+    steps:
+      - checkout
+      - create-env:
+          os: "ubuntu2004"
+          platform: "x86_64"
+          cudnn-version: << parameters.cudnn-version >>
+          trt-version-short:  << parameters.trt-version-short >>
+          bazel-platform: "x86_64"
+      - install-cmake:
+          version: 3.24.1
+      - install-torch-from-index:
+          torch-build:  << parameters.torch-build >>
+          torch-build-index: << parameters.torch-build-index >>
+      - build-cmake
+      - run:
+          name: Move to cmake build dir
+          command: |
+            mkdir -p /tmp/dist/cmake
+            cp -r /tmp/dist/builds/* /tmp/dist/cmake
+      - persist_to_workspace:
+          root: /tmp/dist
+          paths:
+            - cmake
+      - store_artifacts:
+          path: /tmp/dist/cmake
+          destination: x86_64-cmake
+
+  build-x86_64-pyt-ngc:
+    parameters:
+      torch-base-image:
+        type: string
+    machine:
+      image: ubuntu-2004-cuda-11.4:202110-01
+    resource_class: xlarge
+    steps:
+      - checkout
+      - build-py-ngc:
+          release: false
+          torch-base-image: << parameters.torch-base-image >>
+      - run:
+          name: Move to NGC dir
+          command: |
+            mkdir -p /tmp/dist/ngc
+            cp -r /tmp/dist/builds/* /tmp/dist/ngc
+      - persist_to_workspace:
+          root: /tmp/dist
+          paths:
+            - ngc
+      - store_artifacts:
+          path: /tmp/dist/ngc
+          destination: x86_64-pyt-ngc
+
+  package-x86_64-ngc:
+    parameters:
+      enabled:
+        type: boolean
+        default: false
+      torch-base-image:
+        type: string
+    machine:
+      image: ubuntu-2004-cuda-11.4:202110-01
+    resource_class: xlarge
+    steps:
+      - when:
+          condition: << parameters.enabled >>
+          steps:
+            - checkout
+            - build-py-ngc:
+                release: true
+                torch-base-image: << parameters.torch-base-image >>
+            - run:
+                name: Collect packages
+                command: |
+                  mkdir -p /tmp/dist/ngc
+                  cp -r ~/project/py/dist/* /tmp/dist/ngc
+            - store_artifacts:
+                path: /tmp/dist/ngc
+                destination: x86_64-ngc-pkgs
+      - unless:
+          condition: << parameters.enabled >>
+          steps:
+            - run:
+                name: Skipped packaging
+                command: echo -e "Packaging stage not enabled"
 
 parameters:
   bazel-version:
@@ -751,10 +997,10 @@ parameters:
   # Nightly platform config
   torch-nightly-build:
     type: string
-    default: "1.13.0.dev20220810+cu113"
+    default: "1.13.0.dev20220825+cu116"
   torch-nightly-build-index:
     type: string
-    default: "https://download.pytorch.org/whl/nightly/cu113"
+    default: "https://download.pytorch.org/whl/nightly/cu116"
   cudnn-nightly-version:
     type: string
     default: "8.4.1"
@@ -763,15 +1009,15 @@ parameters:
     default: "8.4.1"
   trt-nightly-version-long:
     type: string
-    default: "8.4.1.5"
+    default: "8.4.3.1"
 
   # Release platform config
   torch-release-build:
     type: string
-    default: "1.12.0+cu113"
+    default: "1.12.1+cu116"
   torch-release-build-index:
     type: string
-    default: "https://download.pytorch.org/whl/cu113"
+    default: "https://download.pytorch.org/whl/cu116"
   cudnn-release-version:
     type: string
     default: "8.4.1"
@@ -780,15 +1026,15 @@ parameters:
     default: "8.4.1"
   trt-release-version-long:
     type: string
-    default: "8.4.1.5"
+    default: "8.4.3.1"
 
   # Jetson platform config
   torch-jetson-build:
     type: string
-    default: "torch-1.13.0a0+340c4120.nv22.06-cp38-cp38-linux_aarch64.whl"
+    default: "torch-1.13.0a0+08820cb0.nv22.07-cp38-cp38-linux_aarch64.whl"
   jetpack-version:
     type: string
-    default: "50"
+    default: "502"
   cudnn-jetson-version:
     type: string
     default: "8.3.2"
@@ -799,10 +1045,18 @@ parameters:
     type: string
     default: "8.4.1.5"
 
+  torch-ngc-base-image:
+    type: string
+    default: "nvcr.io/nvidia/pytorch:22.07-py3"
+
   enable-packaging:
     type: boolean
     default: false
 
+  enable-ngc-packaging:
+    type: boolean
+    default: false
+
 # Invoke jobs via workflows
 # See: https://circleci.com/docs/2.0/configuration-reference/#workflows
 workflows:
@@ -821,7 +1075,6 @@ workflows:
           jetpack-version: << pipeline.parameters.jetpack-version >>
           python-version: 3.8.10
 
-
       - build-x86_64-pyt-release:
           torch-build: << pipeline.parameters.torch-release-build >>
           torch-build-index: << pipeline.parameters.torch-release-build-index >>
@@ -855,7 +1108,6 @@ workflows:
           requires:
             - build-x86_64-pyt-release
 
-
       - build-x86_64-pyt-nightly:
           torch-build: << pipeline.parameters.torch-nightly-build >>
           torch-build-index: << pipeline.parameters.torch-nightly-build-index >>
@@ -892,16 +1144,11 @@ workflows:
   release:
     when: << pipeline.parameters.enable-packaging >>
     jobs:
-      - build-aarch64-pyt-jetson:
-          torch-build: << pipeline.parameters.torch-jetson-build >>
-          jetpack-version: << pipeline.parameters.jetpack-version >>
-          python-version: 3.8.10
 
       - build-x86_64-pyt-release:
           torch-build: << pipeline.parameters.torch-release-build >>
           torch-build-index: << pipeline.parameters.torch-release-build-index >>
 
-
       - test-core-cpp-x86_64:
           name: test-core-cpp-x86_64-pyt-release
           channel: "release"
@@ -942,9 +1189,26 @@ workflows:
             - test-py-ts-x86_64-pyt-release
             - test-py-fx-x86_64-pyt-release
 
+  ngc-release:
+    when: << pipeline.parameters.enable-ngc-packaging >>
+    jobs:
+      - build-aarch64-pyt-jetson:
+          torch-build: << pipeline.parameters.torch-jetson-build >>
+          jetpack-version: << pipeline.parameters.jetpack-version >>
+          python-version: 3.8.10
+
+      - build-x86_64-pyt-ngc:
+          torch-base-image: << pipeline.parameters.torch-ngc-base-image >>
+
+      - package-x86_64-ngc:
+          torch-base-image: << pipeline.parameters.torch-ngc-base-image >>
+          enabled: << pipeline.parameters.enable-ngc-packaging >>
+          requires:
+            - build-x86_64-pyt-ngc
+
       - package-jetson:
           name: package-release-aarch64-jetson
-          enabled: << pipeline.parameters.enable-packaging >>
+          enabled: << pipeline.parameters.enable-ngc-packaging >>
           torch-build: << pipeline.parameters.torch-jetson-build >>
           jetpack-version: << pipeline.parameters.jetpack-version >>
           python-version: 3.8.10
@@ -958,7 +1222,6 @@ workflows:
           jetpack-version: << pipeline.parameters.jetpack-version >>
           python-version: 3.8.10
 
-
       - build-x86_64-pyt-release:
           torch-build: << pipeline.parameters.torch-release-build >>
           torch-build-index: << pipeline.parameters.torch-release-build-index >>
@@ -1025,3 +1288,17 @@ workflows:
           trt-version-long: << pipeline.parameters.trt-nightly-version-long >>
           requires:
             - build-x86_64-pyt-nightly
+
+      - build-x86_64-cmake:
+          name: build-x86_64-cmake-pyt-nightly
+          torch-build: << pipeline.parameters.torch-nightly-build >>
+          torch-build-index: << pipeline.parameters.torch-nightly-build-index >>
+          trt-version-short: << pipeline.parameters.trt-nightly-version-short >>
+          cudnn-version: << pipeline.parameters.cudnn-nightly-version >>
+
+      - build-x86_64-cmake:
+          name: build-x86_64-cmake-pyt-release
+          torch-build: << pipeline.parameters.torch-release-build >>
+          torch-build-index: << pipeline.parameters.torch-release-build-index >>
+          trt-version-short: << pipeline.parameters.trt-release-version-short >>
+          cudnn-version: << pipeline.parameters.cudnn-release-version >>
diff --git a/README.md b/README.md
index 9cd32c9533..52232dddb4 100644
--- a/README.md
+++ b/README.md
@@ -113,10 +113,10 @@ torch.jit.save(trt_ts_module, "trt_torchscript_module.ts") # save the TRT embedd
 These are the following dependencies used to verify the testcases. Torch-TensorRT can work with other versions, but the tests are not guaranteed to pass.
 
 - Bazel 5.2.0
-- Libtorch 1.12.0 (built with CUDA 11.3)
-- CUDA 11.3
+- Libtorch 1.12.1 (built with CUDA 11.6)
+- CUDA 11.6
 - cuDNN 8.4.1
-- TensorRT 8.4.1.5
+- TensorRT 8.4.3.1
 
 ## Prebuilt Binaries and Wheel files
 
diff --git a/WORKSPACE b/WORKSPACE
index c93c342fc8..002e8ac276 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -56,17 +56,17 @@ new_local_repository(
 http_archive(
     name = "libtorch",
     build_file = "@//third_party/libtorch:BUILD",
-    sha256 = "80f089939de20e68e3fcad4dfa72a26c8bf91b5e77b11042f671f39ebac35865",
+    sha256 = "5a392132fbff9db1482eae72a30f74b09f53a47edf8305fe9688d4ce7ddb0b6b",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/cu113/libtorch-cxx11-abi-shared-with-deps-1.12.0%2Bcu113.zip"],
+    urls = ["https://download.pytorch.org/libtorch/cu116/libtorch-cxx11-abi-shared-with-deps-1.12.1%2Bcu116.zip"],
 )
 
 http_archive(
     name = "libtorch_pre_cxx11_abi",
     build_file = "@//third_party/libtorch:BUILD",
-    sha256 = "8e35371403f7052d9e9b43bcff383980dbde4df028986dc1dab539953481d55f",
+    sha256 = "5e044cc56a29cd4f3a7198c0fe5b2f0fa8f4c38cd71a0491274b6a914e8f24a7",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/cu113/libtorch-shared-with-deps-1.12.0%2Bcu113.zip"],
+    urls = ["https://download.pytorch.org/libtorch/cu116/libtorch-shared-with-deps-1.12.1%2Bcu116.zip"],
 )
 
 # Download these tarballs manually from the NVIDIA website
@@ -86,10 +86,10 @@ http_archive(
 http_archive(
     name = "tensorrt",
     build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "8107861af218694130f170e071f49814fa3e27f1386ce7cb6d807ac05a7fcf0e",
-    strip_prefix = "TensorRT-8.4.1.5",
+    sha256 = "8d7c2085c1639dcc73875048c23598a8526ce3089136876e31d90258e49e4f61",
+    strip_prefix = "TensorRT-8.4.3.1",
     urls = [
-        "https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.4.1/tars/tensorrt-8.4.1.5.linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz",
+        "https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.4.3/tars/tensorrt-8.4.3.1.linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz",
     ],
 )
 
diff --git a/core/conversion/converters/impl/expand.cpp b/core/conversion/converters/impl/expand.cpp
index e090261a2a..e379614ad3 100644
--- a/core/conversion/converters/impl/expand.cpp
+++ b/core/conversion/converters/impl/expand.cpp
@@ -282,6 +282,116 @@ auto expand_registrations TORCHTRT_UNUSED =
                auto out = ctx->AssociateValueAndTensor(n->outputs()[0], in);
 
                LOG_DEBUG("Repeat layer output tensor shape: " << out->getDimensions());
+               return true;
+             }})
+        .pattern(
+            {"aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, *, int? output_size=None) -> (Tensor)",
+             [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+               auto self = args[0].ITensorOrFreeze(ctx);
+               auto repeats = args[1].unwrapToScalar().to<int>();
+
+               auto input_shape = self->getDimensions();
+
+               int dim;
+               if (args[2].IValue()->isNone()) {
+                 dim = 0;
+
+                 // Flatten self tensor
+                 int size;
+                 if (ctx->input_is_dynamic) {
+                   // Set size to -1 if input is dynamic
+                   size = -1;
+                 } else {
+                   size = 1;
+                   for (int i = 0; i < input_shape.nbDims; i++) {
+                     size *= input_shape.d[i];
+                   }
+                 }
+                 auto flatten = ctx->net->addShuffle(*self);
+                 TORCHTRT_CHECK(flatten, "Unable to create shuffle layer from node: " << *n);
+                 flatten->setReshapeDimensions(util::toDims(std::vector<int64_t>({size})));
+                 self = flatten->getOutput(0);
+                 input_shape = self->getDimensions();
+               } else {
+                 dim = args[2].unwrapToScalar().to<int>();
+               }
+
+               if (ctx->input_is_dynamic) {
+                 int dynamic_dims = 0;
+                 for (int idx = 0; idx < input_shape.nbDims; idx++) {
+                   if (input_shape.d[idx] == -1) {
+                     dynamic_dims++;
+                   }
+                 }
+
+                 if (dynamic_dims > 1) {
+                   TORCHTRT_THROW_ERROR(
+                       "Repeat_interleave is currently not supported when target shape contains more than one dynamic dimension");
+                 }
+               }
+
+               // Insert singleton dimension after desired repeat dimension
+               std::vector<int64_t> repeat_shape_vec;
+               for (int j = 0; j < input_shape.nbDims; j++) {
+                 repeat_shape_vec.push_back(input_shape.d[j]);
+                 if (j == dim) {
+                   repeat_shape_vec.push_back(1);
+                 }
+               }
+               auto expand = ctx->net->addShuffle(*self);
+               TORCHTRT_CHECK(expand, "Unable to create shuffle layer from node: " << *n);
+               auto repeat_shape_dims = util::toDims(repeat_shape_vec);
+               expand->setReshapeDimensions(repeat_shape_dims);
+
+               // Expand on newly created singleton dimension
+               repeat_shape_dims.d[dim + 1] = repeats;
+               std::vector<int64_t> start_vec(repeat_shape_dims.nbDims, 0);
+               auto start_dims = util::toDims(start_vec);
+
+               std::vector<int64_t> strides_vec(repeat_shape_dims.nbDims, 1);
+               strides_vec[dim + 1] = 0;
+               auto strides_dims = util::toDims(strides_vec);
+
+               auto slice = ctx->net->addSlice(*expand->getOutput(0), start_dims, repeat_shape_dims, strides_dims);
+
+               if (ctx->input_is_dynamic) {
+                 auto start_tensor = tensor_to_const(ctx, torch::tensor(start_vec, torch::kInt32));
+
+                 auto expand_output_shape = ctx->net->addShape(*expand->getOutput(0))->getOutput(0);
+                 std::vector<int64_t> repeat_const_vec(repeat_shape_dims.nbDims, 1);
+                 repeat_const_vec[dim + 1] = repeats;
+                 auto repeat_const = tensor_to_const(ctx, torch::tensor(repeat_const_vec, torch::kInt32));
+                 auto repeat_shape_tensor =
+                     ctx->net
+                         ->addElementWise(*expand_output_shape, *repeat_const, nvinfer1::ElementWiseOperation::kPROD)
+                         ->getOutput(0);
+
+                 auto strides_tensor = tensor_to_const(ctx, torch::tensor(strides_vec, torch::kInt32));
+                 slice->setInput(1, *start_tensor);
+                 slice->setInput(2, *repeat_shape_tensor);
+                 slice->setInput(3, *strides_tensor);
+               }
+
+               // Collapse repeated dimension back into desired dimension
+               std::vector<int64_t> collapse_shape_vec;
+               for (int k = 0; k < repeat_shape_dims.nbDims; k++) {
+                 if (k == dim) {
+                   int64_t collapse_dim = repeat_shape_dims.d[k] * repeat_shape_dims.d[++k];
+                   // Set dim size to -1 if repeat is being done on dynamic dim
+                   collapse_dim = std::max(collapse_dim, (int64_t)-1);
+                   collapse_shape_vec.push_back(collapse_dim);
+                 } else {
+                   collapse_shape_vec.push_back(repeat_shape_dims.d[k]);
+                 }
+               }
+               auto collapse = ctx->net->addShuffle(*slice->getOutput(0));
+               TORCHTRT_CHECK(collapse, "Unable to create shuffle layer from node: " << *n);
+               collapse->setReshapeDimensions(util::toDims(collapse_shape_vec));
+
+               collapse->setName(util::node_info(n).c_str());
+               auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], collapse->getOutput(0));
+               LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+
                return true;
              }});
 
diff --git a/core/conversion/converters/impl/select.cpp b/core/conversion/converters/impl/select.cpp
index 8025a1086b..58f0aeae61 100644
--- a/core/conversion/converters/impl/select.cpp
+++ b/core/conversion/converters/impl/select.cpp
@@ -267,41 +267,234 @@ auto select_registrations TORCHTRT_UNUSED =
         .pattern(
             {"aten::index.Tensor(Tensor self, Tensor?[] indices) -> (Tensor)",
              [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+               // refer to https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic_opset9.py#L4627
                auto in = args[0].ITensorOrFreeze(ctx);
                auto ts = args[1].IValue()->toListRef();
 
                std::vector<nvinfer1::ITensor*> tensors;
-               for (auto t : ts) {
+               std::vector<int32_t> adv_idx_indices;
+               for (auto i = 0; i < ts.size(); i++) {
+                 auto t = ts[i];
                  if (t.isTensor()) {
-                   auto torch_tensor = t.toTensor();
+                   auto torch_tensor = t.toTensor().to(torch::kInt32);
                    tensors.push_back(tensor_to_const(ctx, torch_tensor));
+                   adv_idx_indices.push_back(i);
                  } else {
-                   auto cont = t.toCustomClass<TensorContainer>();
-                   tensors.push_back(cont->tensor());
+                   // IValue
+                   if (!t.isNone()) {
+                     adv_idx_indices.push_back(i);
+                     auto cont = t.toCustomClass<TensorContainer>();
+                     // Set datatype for indices tensor to INT32
+                     auto identity = ctx->net->addIdentity(*cont->tensor());
+                     identity->setOutputType(0, nvinfer1::DataType::kINT32);
+                     tensors.push_back(identity->getOutput(0));
+                   }
                  }
                }
 
-               // In TorchScript, aten::index.Tensor indexes the self tensor along its each dimension by several
-               // indexes. In this version of Torch-TensorRT, it can only receive one index tensor which means it only
-               // indexes the self tensor along dimension 0.
-               TORCHTRT_CHECK(
-                   tensors.size() == 1,
-                   "In this version of Torch-TensorRT, aten::index.Tensor can only receive one index tensor which means it only indexes the self tensor along dimension 0.");
-               auto indicesTensor = tensors[0];
-               // Set datatype for indices tensor to INT32
-               auto identity = ctx->net->addIdentity(*indicesTensor);
-               identity->setOutputType(0, nvinfer1::DataType::kINT32);
-               indicesTensor = identity->getOutput(0);
+               if (tensors.size() == 0) {
+                 auto identity_out = ctx->net->addIdentity(*in)->getOutput(0);
+                 auto out = ctx->AssociateValueAndTensor(n->outputs()[0], identity_out);
+                 LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+               } else if (tensors.size() == 1) {
+                 auto indicesTensor = tensors[0];
+                 // Set datatype for indices tensor to INT32
+                 auto identity = ctx->net->addIdentity(*indicesTensor);
+                 identity->setOutputType(0, nvinfer1::DataType::kINT32);
+                 indicesTensor = identity->getOutput(0);
+
+                 // IGatherLayer takes in input tensor, the indices, and the axis of input tensor to take indices
+                 // from
+                 auto gather_layer = ctx->net->addGather(*in, *indicesTensor, 0);
+                 TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
+                 auto gather_out = gather_layer->getOutput(0);
+
+                 auto out = ctx->AssociateValueAndTensor(n->outputs()[0], gather_out);
+                 LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+               } else {
+                 auto inDims = in->getDimensions();
+                 int rank = inDims.nbDims;
+                 LOG_WARNING("If indices include negative values, the exported graph will produce incorrect results.");
+                 int adv_idx_count = adv_idx_indices.size();
+                 auto in_shape_itensor = ctx->net->addShape(*in)->getOutput(0);
+
+                 std::vector<nvinfer1::ITensor*> dim_tensor_list;
+                 for (int i = 0; i < rank; i++) {
+                   auto dim_tensor =
+                       ctx->net
+                           ->addGather(*in_shape_itensor, *tensor_to_const(ctx, torch::tensor({i}, torch::kInt32)), 0)
+                           ->getOutput(0);
+                   dim_tensor_list.push_back(dim_tensor);
+                 }
 
-               // IGatherLayer takes in input tensor, the indices, and the axis of input tensor to take indices
-               // from
-               auto gather_layer = ctx->net->addGather(*in, *indicesTensor, 0);
-               TORCHTRT_CHECK(gather_layer, "Unable to create gather layer from node: " << *n);
-               auto gather_out = gather_layer->getOutput(0);
+                 // t: [x_1, y_1, y_2, ..., x_m, ..., y_n] -> t: [x_1, x_2, ..., x_m, y_1, y_2, ..., y_n],
+                 // where t is a tensor of rank m+n, {x_i} are axes where tensor index is provided, and {y_i} are axes
+                 // for ":".
+                 auto in_transpose_layer = ctx->net->addShuffle(*in);
+                 TORCHTRT_CHECK(in_transpose_layer, "Unable to create shuffle layer from node: " << *n);
+                 nvinfer1::Permutation permute;
+                 std::vector<int32_t> new_order;
+                 for (int i = 0; i < adv_idx_count; i++) {
+                   new_order.push_back(adv_idx_indices[i]);
+                 }
+                 for (int i = 0; i < rank; i++) {
+                   if (std::find(adv_idx_indices.begin(), adv_idx_indices.end(), i) == adv_idx_indices.end()) {
+                     new_order.push_back(i);
+                   }
+                 }
+                 std::copy(new_order.begin(), new_order.end(), permute.order);
+                 in_transpose_layer->setSecondTranspose(permute);
+                 auto shuffle_out = in_transpose_layer->getOutput(0);
+
+                 //  t: [x_1, x_2, ..., x_m, y_1, y_2, ..., y_n] -> t: [x_1*x_2* ...*x_m, y_1*y_2* ...*y_n]
+                 nvinfer1::ITensor* flatten_tensor = NULL;
+                 {
+                   auto shuffle_shape_tensor = ctx->net->addShape(*shuffle_out)->getOutput(0);
+                   auto d0 = tensor_to_const(ctx, torch::tensor({1}, torch::kInt32));
+                   for (int i = 0; i < adv_idx_count; i++) {
+                     auto dim_tensor =
+                         ctx->net
+                             ->addGather(
+                                 *shuffle_shape_tensor, *tensor_to_const(ctx, torch::tensor({i}, torch::kInt32)), 0)
+                             ->getOutput(0);
+                     d0 = add_elementwise(
+                              ctx,
+                              nvinfer1::ElementWiseOperation::kPROD,
+                              d0,
+                              dim_tensor,
+                              std::string("compute_dim0_") + std::to_string(i))
+                              ->getOutput(0);
+                   }
 
-               auto out = ctx->AssociateValueAndTensor(n->outputs()[0], gather_out);
+                   auto d1 = tensor_to_const(ctx, torch::tensor({1}, torch::kInt32));
+                   for (int i = adv_idx_count; i < rank; i++) {
+                     auto dim_tensor =
+                         ctx->net
+                             ->addGather(
+                                 *shuffle_shape_tensor, *tensor_to_const(ctx, torch::tensor({i}, torch::kInt32)), 0)
+                             ->getOutput(0);
+                     d1 = add_elementwise(
+                              ctx,
+                              nvinfer1::ElementWiseOperation::kPROD,
+                              d1,
+                              dim_tensor,
+                              std::string("compute_dim1_") + std::to_string(i))
+                              ->getOutput(0);
+                   }
 
-               LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+                   std::vector<nvinfer1::ITensor*> concat_tensors;
+                   concat_tensors.push_back(d0);
+                   concat_tensors.push_back(d1);
+                   auto concat_layer = ctx->net->addConcatenation(concat_tensors.data(), concat_tensors.size());
+
+                   auto shuffle = ctx->net->addShuffle(*shuffle_out);
+                   shuffle->setInput(1, *concat_layer->getOutput(0));
+                   flatten_tensor = shuffle->getOutput(0);
+                   LOG_DEBUG(flatten_tensor->getDimensions());
+                 }
+
+                 // tensor index = \sum_{i=1}^m (ind_i * \prod_{j=i+1}^m (x_j)),  ind_i is input indices[i], x_j is the
+                 // j dimension of input x.
+                 nvinfer1::ITensor* multiplier = dim_tensor_list[adv_idx_indices[adv_idx_count - 1]];
+                 nvinfer1::ITensor* cum_adv_index = tensors[adv_idx_count - 1];
+                 for (int i = adv_idx_count - 2; i >= 0; i--) {
+                   nvinfer1::ITensor* adv_index = add_elementwise(
+                                                      ctx,
+                                                      nvinfer1::ElementWiseOperation::kPROD,
+                                                      tensors[i],
+                                                      multiplier,
+                                                      std::string("adv_index_") + std::to_string(i))
+                                                      ->getOutput(0);
+                   cum_adv_index = add_elementwise(
+                                       ctx,
+                                       nvinfer1::ElementWiseOperation::kSUM,
+                                       cum_adv_index,
+                                       adv_index,
+                                       std::string("cum_adv_index_") + std::to_string(i))
+                                       ->getOutput(0);
+                   multiplier = add_elementwise(
+                                    ctx,
+                                    nvinfer1::ElementWiseOperation::kPROD,
+                                    multiplier,
+                                    dim_tensor_list[adv_idx_indices[i]],
+                                    std::string("multiplier_") + std::to_string(i))
+                                    ->getOutput(0);
+                 }
+
+                 // perform gather
+                 auto gather_out = ctx->net->addGather(*flatten_tensor, *cum_adv_index, 0)->getOutput(0);
+
+                 nvinfer1::ITensor* reshape_output = NULL;
+                 {
+                   auto cum_adv_index_shape_tensor = ctx->net->addShape(*cum_adv_index)->getOutput(0);
+                   // check if all advanced indices are consecutive.
+                   if (adv_idx_count == (adv_idx_indices[adv_idx_count - 1] - adv_idx_indices[0] + 1)) {
+                     // unfold regular index axes
+                     std::vector<nvinfer1::ITensor*> concat_tensors;
+                     concat_tensors.push_back(tensor_to_const(ctx, torch::tensor({-1}, torch::kInt32)));
+                     for (int i = 0; i < rank; i++) {
+                       if (std::find(adv_idx_indices.begin(), adv_idx_indices.end(), i) == adv_idx_indices.end()) {
+                         nvinfer1::ITensor* current_dim = dim_tensor_list[i];
+                         concat_tensors.push_back(current_dim);
+                       }
+                     }
+                     auto concat_layer = ctx->net->addConcatenation(concat_tensors.data(), concat_tensors.size());
+                     auto regular_index_shuffle_layer = ctx->net->addShuffle(*gather_out);
+                     regular_index_shuffle_layer->setInput(1, *concat_layer->getOutput(0));
+                     auto unfold_tensor = regular_index_shuffle_layer->getOutput(0);
+
+                     // Transpose folded advanced indexed axis to its original location.
+                     auto transpose_advanced_shuffle_layer = ctx->net->addShuffle(*unfold_tensor);
+                     nvinfer1::Permutation permute;
+                     std::vector<int32_t> new_order;
+                     for (int i = 1; i < adv_idx_indices[0] + 1; i++) {
+                       new_order.push_back(i);
+                     }
+                     new_order.push_back(0);
+                     for (int i = adv_idx_indices[0] + 1; i < rank - adv_idx_count + 1; i++) {
+                       new_order.push_back(i);
+                     }
+                     std::copy(new_order.begin(), new_order.end(), permute.order);
+                     transpose_advanced_shuffle_layer->setSecondTranspose(permute);
+                     auto shuffle_out = transpose_advanced_shuffle_layer->getOutput(0);
+
+                     // unfold advanced index axes
+                     std::vector<nvinfer1::ITensor*> concat_final_tensors;
+                     for (int i = 0; i < adv_idx_indices[0]; i++) {
+                       nvinfer1::ITensor* current_dim = dim_tensor_list[i];
+                       concat_final_tensors.push_back(current_dim);
+                     }
+                     concat_final_tensors.push_back(cum_adv_index_shape_tensor);
+                     for (int i = adv_idx_indices[0]; i < rank; i++) {
+                       if (std::find(adv_idx_indices.begin(), adv_idx_indices.end(), i) == adv_idx_indices.end()) {
+                         nvinfer1::ITensor* current_dim = dim_tensor_list[i];
+                         concat_final_tensors.push_back(current_dim);
+                       }
+                     }
+                     auto concat_final_shape_layer =
+                         ctx->net->addConcatenation(concat_final_tensors.data(), concat_final_tensors.size());
+                     auto unfold_advanced_shuffle_layer = ctx->net->addShuffle(*shuffle_out);
+                     unfold_advanced_shuffle_layer->setInput(1, *concat_final_shape_layer->getOutput(0));
+                     reshape_output = unfold_advanced_shuffle_layer->getOutput(0);
+                   } else {
+                     std::vector<nvinfer1::ITensor*> concat_tensors;
+                     concat_tensors.push_back(cum_adv_index_shape_tensor);
+                     for (int i = 0; i < rank; i++) {
+                       if (std::find(adv_idx_indices.begin(), adv_idx_indices.end(), i) == adv_idx_indices.end()) {
+                         nvinfer1::ITensor* current_dim = dim_tensor_list[i];
+                         concat_tensors.push_back(current_dim);
+                       }
+                     }
+                     auto concat_layer = ctx->net->addConcatenation(concat_tensors.data(), concat_tensors.size());
+                     auto shuffle_layer = ctx->net->addShuffle(*gather_out);
+                     shuffle_layer->setInput(1, *concat_layer->getOutput(0));
+                     reshape_output = shuffle_layer->getOutput(0);
+                   }
+                 }
+
+                 auto out = ctx->AssociateValueAndTensor(n->outputs()[0], reshape_output);
+                 LOG_DEBUG("Output tensor shape: " << out->getDimensions());
+               }
                return true;
              }})
         .pattern(
diff --git a/core/conversion/evaluators/prim.cpp b/core/conversion/evaluators/prim.cpp
index 2245ca05dc..81a7bb9991 100644
--- a/core/conversion/evaluators/prim.cpp
+++ b/core/conversion/evaluators/prim.cpp
@@ -100,7 +100,12 @@ auto prim_registrations =
                      auto ival = c10::IValue(std::move(c10::make_intrusive<TensorContainer>(tensor_holder)));
                      list.emplace_back(std::move(ival));
                    } else {
-                     list.emplace_back(std::move(args.at(in).unwrapToTensor()));
+                     if (args.at(in).IValue()->isNone()) {
+                       auto ival = torch::jit::IValue();
+                       list.emplace_back(std::move(ival));
+                     } else {
+                       list.emplace_back(std::move(args.at(in).unwrapToTensor()));
+                     }
                    }
                  }
                  return c10::optional<torch::jit::IValue>(std::move(torch::jit::IValue(list)));
diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html b/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html
index 6c296818b0..c5baedfa0a 100644
--- a/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html
+++ b/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html b/docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html
index dc6319f769..ee631a4842 100644
--- a/docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html
+++ b/docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html
@@ -199,7 +199,7 @@
               
               
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+                  master (1.2.0a0+51a991e)
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diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html b/docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html
index b7aed51b34..6c81106e8e 100644
--- a/docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html
+++ b/docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html b/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html
index 893d077356..ae03830ea8 100644
--- a/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html
+++ b/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html b/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html
index e6c620c5ce..8a782a7aa9 100644
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+++ b/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html
@@ -199,7 +199,7 @@
               
               
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-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docs/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html b/docs/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html
index 5fb653946f..3d01828515 100644
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+++ b/docs/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html
@@ -199,7 +199,7 @@
               
               
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+                  master (1.2.0a0+51a991e)
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diff --git a/docs/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html b/docs/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html
index 5bd4c83533..602c0b350a 100644
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@@ -199,7 +199,7 @@
               
               
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+                  master (1.2.0a0+51a991e)
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diff --git a/docs/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html b/docs/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html
index 4172371cce..38e962e08c 100644
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+++ b/docs/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html
@@ -199,7 +199,7 @@
               
               
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+                  master (1.2.0a0+51a991e)
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diff --git a/docs/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html b/docs/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html
index f2d5dee85a..9a1fa322c1 100644
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+++ b/docs/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docs/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html b/docs/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html
index df6c659561..a5b3fec8be 100644
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+++ b/docs/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html
@@ -199,7 +199,7 @@
               
               
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diff --git a/docs/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html b/docs/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html
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diff --git a/docs/_sources/cli/torchtrtc.rst.txt b/docs/_sources/cli/torchtrtc.rst.txt
index 68f599a5cd..69292c491c 100644
--- a/docs/_sources/cli/torchtrtc.rst.txt
+++ b/docs/_sources/cli/torchtrtc.rst.txt
@@ -135,12 +135,15 @@ e.g.
 
     torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous" -p f16
 
+- To include a set of custom operators
 
-To run with custom torch operators
 .. code-block:: shell
-torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-torch-ops=<path to custom library> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
 
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-torch-ops=<path to custom library .so file> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
+
+
+- To include a set of custom converters
 
-To run with custom converters
 .. code-block:: shell
-torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-converters=<path to custom library> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-converters=<path to custom library .so file> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
diff --git a/docs/cli/torchtrtc.html b/docs/cli/torchtrtc.html
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@@ -476,12 +476,18 @@
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts <span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous&quot;</span> -p f16
 </pre></div>
 </div>
-<p>To run with custom torch operators
-.. code-block:: shell
-torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts –custom-torch-ops=&lt;path to custom library&gt; “[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]&#64;fp16%contiguous” -p f16</p>
-<p>To run with custom converters
-.. code-block:: shell
-torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts –custom-converters=&lt;path to custom library&gt; “[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]&#64;fp16%contiguous” -p f16</p>
+<ul class="simple">
+<li><p>To include a set of custom operators</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-torch-ops<span class="o">=</span>&lt;path to custom library .so file&gt; <span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous&quot;</span> -p f16
+</pre></div>
+</div>
+<ul class="simple">
+<li><p>To include a set of custom converters</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-converters<span class="o">=</span>&lt;path to custom library .so file&gt; <span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous&quot;</span> -p f16
+</pre></div>
+</div>
 </section>
 
 
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@@ -567,7 +567,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch_tensorrt.ts.TensorRTCompileSpec">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">&lt;torch.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f7cd523a630&gt;</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">&lt;torch.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f89ba6d5eb0&gt;</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</p>
 <dl class="field-list simple">
 <dt class="field-odd">Keyword Arguments</dt>
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diff --git a/docs/src/pytorch-sphinx-theme/docs/changelog.html b/docs/src/pytorch-sphinx-theme/docs/changelog.html
index 7c83b89f2f..1c06b101c5 100644
--- a/docs/src/pytorch-sphinx-theme/docs/changelog.html
+++ b/docs/src/pytorch-sphinx-theme/docs/changelog.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docs/src/pytorch-sphinx-theme/docs/configuring.html b/docs/src/pytorch-sphinx-theme/docs/configuring.html
index 547dc23e5e..f1c5a3d2e0 100644
--- a/docs/src/pytorch-sphinx-theme/docs/configuring.html
+++ b/docs/src/pytorch-sphinx-theme/docs/configuring.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/api.html b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
index 39060399bf..af2ed1dfe0 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/api.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
index b84bbdb827..9d344d63bc 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
@@ -544,7 +544,7 @@ <h3><a class="toc-backref" href="#id41"><span class="section-number">3.4.4. </sp
 <section id="code-blocks">
 <h3><a class="toc-backref" href="#id42"><span class="section-number">3.4.5. </span>Code Blocks</a><a class="headerlink" href="#code-blocks" title="Permalink to this headline">¶</a></h3>
 <pre class="literal-block"># parsed-literal test
-curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0a0+c83e09e).tar-gz</pre>
+curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0a0+51a991e).tar-gz</pre>
 <div class="literal-block-wrapper docutils container" id="id23">
 <div class="code-block-caption"><span class="caption-text">Code Blocks can have captions.</span><a class="headerlink" href="#id23" title="Permalink to this code">¶</a></div>
 <div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="w"></span>
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
index 2636a83008..5695156066 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/long.html b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
index bf2e2943c8..6371569524 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/long.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
@@ -197,7 +197,7 @@
               
               
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-                  master (1.2.0a0+c83e09e)
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
index 62116d88fc..40aff14ed5 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
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diff --git a/docs/src/pytorch-sphinx-theme/docs/index.html b/docs/src/pytorch-sphinx-theme/docs/index.html
index 1d75287a59..b03537a762 100644
--- a/docs/src/pytorch-sphinx-theme/docs/index.html
+++ b/docs/src/pytorch-sphinx-theme/docs/index.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/installing.html b/docs/src/pytorch-sphinx-theme/docs/installing.html
index d19cde5180..7ce3043701 100644
--- a/docs/src/pytorch-sphinx-theme/docs/installing.html
+++ b/docs/src/pytorch-sphinx-theme/docs/installing.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/tutorials/creating_torchscript_module_in_python.html b/docs/tutorials/creating_torchscript_module_in_python.html
index 10e7006041..bdb3cf0426 100644
--- a/docs/tutorials/creating_torchscript_module_in_python.html
+++ b/docs/tutorials/creating_torchscript_module_in_python.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docs/tutorials/getting_started_with_fx_path.html b/docs/tutorials/getting_started_with_fx_path.html
index 64d23eb2d1..936f983a04 100644
--- a/docs/tutorials/getting_started_with_fx_path.html
+++ b/docs/tutorials/getting_started_with_fx_path.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/tutorials/notebooks.html b/docs/tutorials/notebooks.html
index 22de93a025..f4c8dbda7b 100644
--- a/docs/tutorials/notebooks.html
+++ b/docs/tutorials/notebooks.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/tutorials/ptq.html b/docs/tutorials/ptq.html
index c0db24a4fc..6c48abaf2b 100644
--- a/docs/tutorials/ptq.html
+++ b/docs/tutorials/ptq.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/tutorials/runtime.html b/docs/tutorials/runtime.html
index da0cffcfd1..274b80dce3 100644
--- a/docs/tutorials/runtime.html
+++ b/docs/tutorials/runtime.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docs/tutorials/serving_torch_tensorrt_with_triton.html b/docs/tutorials/serving_torch_tensorrt_with_triton.html
index 2b3067b538..732b18ef42 100644
--- a/docs/tutorials/serving_torch_tensorrt_with_triton.html
+++ b/docs/tutorials/serving_torch_tensorrt_with_triton.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/tutorials/use_from_pytorch.html b/docs/tutorials/use_from_pytorch.html
index b53b7b749c..e9e8e75342 100644
--- a/docs/tutorials/use_from_pytorch.html
+++ b/docs/tutorials/use_from_pytorch.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
                 </div>
               
             
diff --git a/docs/tutorials/using_dla.html b/docs/tutorials/using_dla.html
index 00ae235e69..6252d51262 100644
--- a/docs/tutorials/using_dla.html
+++ b/docs/tutorials/using_dla.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+c83e09e)
+                  master (1.2.0a0+51a991e)
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diff --git a/docsrc/cli/torchtrtc.rst b/docsrc/cli/torchtrtc.rst
index 68f599a5cd..69292c491c 100644
--- a/docsrc/cli/torchtrtc.rst
+++ b/docsrc/cli/torchtrtc.rst
@@ -135,12 +135,15 @@ e.g.
 
     torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous" -p f16
 
+- To include a set of custom operators
 
-To run with custom torch operators
 .. code-block:: shell
-torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-torch-ops=<path to custom library> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
 
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-torch-ops=<path to custom library .so file> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
+
+
+- To include a set of custom converters
 
-To run with custom converters
 .. code-block:: shell
-torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-converters=<path to custom library> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-converters=<path to custom library .so file> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
diff --git a/py/requirements.txt b/py/requirements.txt
index 5254a658a4..27aca2544f 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -1,6 +1,6 @@
 numpy
 pybind11==2.6.2
---extra-index-url https://download.pytorch.org/whl/cu113
-torch==1.12.0
+--extra-index-url https://download.pytorch.org/whl/cu116
+torch==1.12.1
 --extra-index-url https://pypi.ngc.nvidia.com
-nvidia-tensorrt==8.4.1.5
+nvidia-tensorrt==8.4.3.1
diff --git a/tests/core/conversion/converters/test_expand.cpp b/tests/core/conversion/converters/test_expand.cpp
index bf62266f32..53630b661a 100644
--- a/tests/core/conversion/converters/test_expand.cpp
+++ b/tests/core/conversion/converters/test_expand.cpp
@@ -445,3 +445,227 @@ TEST(Converters, ATenRepeatExtraDimsConvertsCorrectlyWithDynamicInput) {
 
   ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
 }
+
+TEST(Converters, ATenRepeatInterleaveScalarDimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+            %2 : int = prim::Constant[value=3]()
+            %3 : int = prim::Constant[value=1]()
+            %4 : None = prim::Constant()
+            %5 : Tensor = aten::repeat_interleave(%x.1, %2, %3, %4)
+            return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {1, 3}, {at::kCUDA});
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {trt_in});
+
+  auto trt = trt_results[0].reshape(jit_results[0].sizes());
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
+
+TEST(Converters, ATenRepeatInterleaveScalarDimConvertsCorrectlyWithDynamicInput) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+            %2 : int = prim::Constant[value=3]()
+            %3 : int = prim::Constant[value=1]()
+            %4 : None = prim::Constant()
+            %5 : Tensor = aten::repeat_interleave(%x.1, %2, %3, %4)
+            return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {1, 3}, {at::kCUDA});
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngineDynamic(g, params, {trt_in});
+
+  auto trt = trt_results[0].reshape(jit_results[0].sizes());
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
+
+TEST(Converters, ATenRepeatInterleaveScalarNoDimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+            %2 : int = prim::Constant[value=3]()
+            %3 : None = prim::Constant()
+            %4 : None = prim::Constant()
+            %5 : Tensor = aten::repeat_interleave(%x.1, %2, %3, %4)
+            return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {1, 3}, {at::kCUDA});
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {trt_in});
+
+  auto trt = trt_results[0].reshape(jit_results[0].sizes());
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
+
+TEST(Converters, ATenRepeatInterleaveScalarNoDimConvertsCorrectlyWithDynamicInput) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+            %2 : int = prim::Constant[value=3]()
+            %3 : None = prim::Constant()
+            %4 : None = prim::Constant()
+            %5 : Tensor = aten::repeat_interleave(%x.1, %2, %3, %4)
+            return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {1, 3}, {at::kCUDA});
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngineDynamic(g, params, {trt_in});
+
+  auto trt = trt_results[0].reshape(jit_results[0].sizes());
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
+
+TEST(Converters, ATenRepeatInterleave3dScalarDimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+            %2 : int = prim::Constant[value=3]()
+            %3 : int = prim::Constant[value=1]()
+            %4 : None = prim::Constant()
+            %5 : Tensor = aten::repeat_interleave(%x.1, %2, %3, %4)
+            return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {2, 3, 2}, {at::kCUDA});
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {trt_in});
+
+  auto trt = trt_results[0].reshape(jit_results[0].sizes());
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
+
+TEST(Converters, ATenRepeatInterleave3dScalarDimConvertsCorrectlyWithDynamicInput) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+            %2 : int = prim::Constant[value=3]()
+            %3 : int = prim::Constant[value=1]()
+            %4 : None = prim::Constant()
+            %5 : Tensor = aten::repeat_interleave(%x.1, %2, %3, %4)
+            return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {2, 3, 2}, {at::kCUDA});
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngineDynamic(g, params, {trt_in});
+
+  auto trt = trt_results[0].reshape(jit_results[0].sizes());
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
+
+TEST(Converters, ATenRepeatInterleave3dScalarNoDimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+            %2 : int = prim::Constant[value=3]()
+            %3 : None = prim::Constant()
+            %4 : None = prim::Constant()
+            %5 : Tensor = aten::repeat_interleave(%x.1, %2, %3, %4)
+            return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {2, 3, 2}, {at::kCUDA});
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {trt_in});
+
+  auto trt = trt_results[0].reshape(jit_results[0].sizes());
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
+
+TEST(Converters, ATenRepeatInterleave3dScalarNoDimConvertsCorrectlyWithDynamicInput) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+            %2 : int = prim::Constant[value=3]()
+            %3 : None = prim::Constant()
+            %4 : None = prim::Constant()
+            %5 : Tensor = aten::repeat_interleave(%x.1, %2, %3, %4)
+            return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {2, 3, 2}, {at::kCUDA});
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngineDynamic(g, params, {trt_in});
+
+  auto trt = trt_results[0].reshape(jit_results[0].sizes());
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
diff --git a/tests/core/conversion/converters/test_select.cpp b/tests/core/conversion/converters/test_select.cpp
index e9405c0155..1285c24dd6 100644
--- a/tests/core/conversion/converters/test_select.cpp
+++ b/tests/core/conversion/converters/test_select.cpp
@@ -776,7 +776,7 @@ TEST(Converters, ATenMaskedFillZerosConvertsCorrectly) {
       torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
 }
 
-TEST(Converters, ATenIndexTensorConvertsCorrectly) {
+TEST(Converters, ATenIndexTensorOneIndiceConvertsCorrectly) {
   const auto graph = R"IR(
       graph(%x.1 : Tensor,
             %index : Tensor):
@@ -802,6 +802,156 @@ TEST(Converters, ATenIndexTensorConvertsCorrectly) {
       torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
 }
 
+TEST(Converters, ATenIndexTensorFullIndicesConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%x.1 : Tensor,
+            %index0 : Tensor,
+            %index1 : Tensor,
+            %index2 : Tensor):
+        %18 : Tensor?[] = prim::ListConstruct(%index0, %index1, %index2)
+        %19 : Tensor = aten::index(%x.1, %18)
+        return (%19))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in1 = at::randint(1, 10, {5, 10, 4}, {at::kCUDA});
+  auto index0 = at::tensor({0, 1, 2, 3}, {at::kCUDA}).to(torch::kLong);
+  auto index1 = at::tensor({1, 3, 4, 6}, {at::kCUDA}).to(torch::kLong);
+  auto index2 = at::tensor({3, 2, 1, 0}, {at::kCUDA}).to(torch::kLong);
+  auto index0_trt = index0.to(torch::kInt32);
+  auto index1_trt = index1.to(torch::kInt32);
+  auto index2_trt = index2.to(torch::kInt32);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in1, index0, index1, index2});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in1, index0_trt, index1_trt, index2_trt});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
+TEST(Converters, ATenIndexTensorIdx0Idx1NoneConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%x.1 : Tensor,
+            %index0 : Tensor,
+            %index1 : Tensor):
+        %5 : NoneType = prim::Constant()
+        %18 : Tensor?[] = prim::ListConstruct(%index0, %index1, %5)
+        %19 : Tensor = aten::index(%x.1, %18)
+        return (%19))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in1 = at::randint(1, 10, {5, 10, 4}, {at::kCUDA});
+  auto index0 = at::tensor({0, 1, 2, 3}, {at::kCUDA}).to(torch::kLong);
+  auto index1 = at::tensor({1, 3, 4, 6}, {at::kCUDA}).to(torch::kLong);
+  auto index0_trt = index0.to(torch::kInt32);
+  auto index1_trt = index1.to(torch::kInt32);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in1, index0, index1});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in1, index0_trt, index1_trt});
+  LOG_DEBUG(trt_results);
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
+TEST(Converters, ATenIndexTensorIdx0NoneIdx1ConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%x.1 : Tensor,
+            %index0 : Tensor,
+            %index1 : Tensor):
+        %5 : NoneType = prim::Constant()
+        %18 : Tensor?[] = prim::ListConstruct(%index0, %5, %index1)
+        %19 : Tensor = aten::index(%x.1, %18)
+        return (%19))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in1 = at::randint(1, 10, {5, 10, 4}, {at::kCUDA});
+  auto index0 = at::tensor({0, 1, 2, 3}, {at::kCUDA}).to(torch::kLong);
+  auto index1 = at::tensor({3, 2, 1, 0}, {at::kCUDA}).to(torch::kLong);
+  auto index0_trt = index0.to(torch::kInt32);
+  auto index1_trt = index1.to(torch::kInt32);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in1, index0, index1});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in1, index0_trt, index1_trt});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
+TEST(Converters, ATenIndexTensorNoneIdx0Idx1ConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%x.1 : Tensor,
+            %index0 : Tensor,
+            %index1 : Tensor):
+        %5 : NoneType = prim::Constant()
+        %18 : Tensor?[] = prim::ListConstruct(%5, %index0, %index1)
+        %19 : Tensor = aten::index(%x.1, %18)
+        return (%19))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in1 = at::randint(1, 10, {5, 10, 4}, {at::kCUDA});
+  auto index0 = at::tensor({0, 1, 2, 3}, {at::kCUDA}).to(torch::kLong);
+  auto index1 = at::tensor({3, 2, 1, 0}, {at::kCUDA}).to(torch::kLong);
+  auto index0_trt = index0.to(torch::kInt32);
+  auto index1_trt = index1.to(torch::kInt32);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in1, index0, index1});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in1, index0_trt, index1_trt});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
+TEST(Converters, ATenIndexTensorIdxsNoneConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%x.1 : Tensor,
+            %index0 : Tensor,
+            %index1 : Tensor,
+            %index2 : Tensor):
+        %5 : NoneType = prim::Constant()
+        %18 : Tensor?[] = prim::ListConstruct(%index0, %index1, %index2, %5)
+        %19 : Tensor = aten::index(%x.1, %18)
+        return (%19))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in1 = at::randint(1, 10, {4, 8, 8, 4}, {at::kCUDA});
+  auto index0 = at::full({4, 13, 1}, 1, {at::kCUDA}).to(torch::kLong);
+  auto index1 = at::full({4, 13, 1}, 2, {at::kCUDA}).to(torch::kLong);
+  auto index2 = at::full({4, 13, 1}, 3, {at::kCUDA}).to(torch::kLong);
+  auto index0_trt = index0.to(torch::kInt32);
+  auto index1_trt = index1.to(torch::kInt32);
+  auto index2_trt = index2.to(torch::kInt32);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in1, index0, index1, index2});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in1, index0_trt, index1_trt, index2_trt});
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
+}
+
 TEST(Converters, ATenUnbindConvertsCorrectly) {
   const auto graph = R"IR(
     graph(%x.1 : Tensor):