Add Face Landmarks Connection constants for the Python API

Merge pull request #4286 from google/revert-4279-face-landmarker-python-add-connection-constants
Revert "Add Face Landmarks Connection constants for the Python API"
2023-04-13 14:19:10 -06:00 · 2023-04-13 14:14:02 -06:00 · 2023-04-14 04:12:33 +08:00 · 2023-04-13 13:05:42 -07:00 · 2023-04-13 11:52:45 -07:00 · 2023-04-13 11:45:04 -07:00
1124 changed files with 99739 additions and 6240 deletions
@@ -1 +1 @@
-5.2.0
+6.1.1
@@ -1,27 +0,0 @@
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@@ -1,51 +0,0 @@
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@@ -0,0 +1,73 @@
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    validations:
      required: false
@@ -0,0 +1,72 @@
 name: Task Issue
 description: Use this template for assistance with using MediaPipe Tasks (developers.google.com/mediapipe/solutions) to deploy on-device ML solutions (e.g. gesture recognition etc.) on supported platforms
 labels: 'type:task'
 body:
  - type: markdown
    id: linkmodel
    attributes:
      value: Please make sure that this is a [Tasks](https://developers.google.com/mediapipe/solutions) issue.
  - type: dropdown
    id: customcode_model
    attributes:
      label: Have I written custom code (as opposed to using a stock example script provided in MediaPipe)
      options:
        - 'Yes'
        - 'No'
    validations:
      required: false
  - type: input
    id: os_model
    attributes:
      label: OS Platform and Distribution
      placeholder: e.g. Linux Ubuntu 16.04, Android 11, iOS 14.4
    validations:
      required: true
  - type: input
    id: task-sdk-version
    attributes:
      label: MediaPipe Tasks SDK version
    validations:
      required: false
  - type: input
    id: taskname
    attributes:
      label: Task name (e.g. Image classification, Gesture recognition etc.)
    validations:
       required: true
  - type: input
    id: programminglang
    attributes:
      label: Programming Language and version (e.g. C++, Python, Java)
    validations:
      required: true
  - type: textarea
    id: current_model
    attributes:
      label: Describe the actual behavior
      render: shell
    validations:
      required: true
  - type: textarea
    id: expected_model
    attributes:
      label: Describe the expected behaviour
      render: shell
    validations:
      required: true
  - type: textarea
    id: what-happened_model
    attributes:
      label: Standalone code/steps you may have used to try to get what you need
      description: If there is a problem, provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab, GitHub repo link or anything that we can use to reproduce the problem
      render: shell
    validations:
       required: true
  - type: textarea
    id: other_info
    attributes:
      label: Other info / Complete Logs
      description: Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached
      render: shell
    validations:
      required: false
@@ -61,7 +61,7 @@ RUN pip3 install tf_slim
 RUN ln -s /usr/bin/python3 /usr/bin/python
 # Install bazel
-ARG BAZEL_VERSION=5.2.0
+ARG BAZEL_VERSION=6.1.1
 RUN mkdir /bazel && \
    wget --no-check-certificate -O /bazel/installer.sh "https://github.com/bazelbuild/bazel/releases/download/${BAZEL_VERSION}/b\
 azel-${BAZEL_VERSION}-installer-linux-x86_64.sh" && \
@@ -199,3 +199,20 @@
   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.
 ===========================================================================
 For files under tasks/cc/text/language_detector/custom_ops/utils/utf/
 ===========================================================================
 /*
 * The authors of this software are Rob Pike and Ken Thompson.
 *              Copyright (c) 2002 by Lucent Technologies.
 * Permission to use, copy, modify, and distribute this software for any
 * purpose without fee is hereby granted, provided that this entire notice
 * is included in all copies of any software which is or includes a copy
 * or modification of this software and in all copies of the supporting
 * documentation for such software.
 * THIS SOFTWARE IS BEING PROVIDED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED
 * WARRANTY.  IN PARTICULAR, NEITHER THE AUTHORS NOR LUCENT TECHNOLOGIES MAKE ANY
 * REPRESENTATION OR WARRANTY OF ANY KIND CONCERNING THE MERCHANTABILITY
 * OF THIS SOFTWARE OR ITS FITNESS FOR ANY PARTICULAR PURPOSE.
 */
@@ -6,6 +6,20 @@ nav_order: 1
 ![MediaPipe](https://mediapipe.dev/images/mediapipe_small.png)
 ----
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 *This notice and web page will be removed on June 1, 2023.*
 ----
 <br><br><br><br><br><br><br><br><br><br>
 <br><br><br><br><br><br><br><br><br><br>
 <br><br><br><br><br><br><br><br><br><br>
 --------------------------------------------------------------------------------
 ## Live ML anywhere
@@ -19,6 +33,8 @@ ML solutions for live and streaming media.
 ![ready_to_use.png](https://mediapipe.dev/images/ready_to_use_small.png)                                                             | ![open_source.png](https://mediapipe.dev/images/open_source_small.png)
 ***Ready-to-use solutions***: *Cutting-edge ML solutions demonstrating full power of the framework*            | ***Free and open source***: *Framework and solutions both under Apache 2.0, fully extensible and customizable*
 ----
 ## ML solutions in MediaPipe
 Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris                                                                                                      | Hands                                                                                                      | Pose                                                                                                      | Holistic
@@ -10,33 +10,31 @@ bind(
 http_archive(
    name = "bazel_skylib",
-    type = "tar.gz",
+    sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506",
    urls = [
-        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.0.3/bazel-skylib-1.0.3.tar.gz",
+        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
-        "https://mirror.bazel.build/github.com/bazelbuild/bazel-skylib/releases/download/1.0.3/bazel-skylib-1.0.3.tar.gz",
+        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
    ],
    sha256 = "1c531376ac7e5a180e0237938a2536de0c54d93f5c278634818e0efc952dd56c",
 )
 load("@bazel_skylib//:workspace.bzl", "bazel_skylib_workspace")
 bazel_skylib_workspace()
 load("@bazel_skylib//lib:versions.bzl", "versions")
 versions.check(minimum_bazel_version = "3.7.2")
-# ABSL cpp library lts_2021_03_24, patch 2.
+# ABSL cpp library lts_2023_01_25.
 http_archive(
    name = "com_google_absl",
    urls = [
-        "https://github.com/abseil/abseil-cpp/archive/refs/tags/20220623.1.tar.gz",
+        "https://github.com/abseil/abseil-cpp/archive/refs/tags/20230125.0.tar.gz",
    ],
    # Remove after https://github.com/abseil/abseil-cpp/issues/326 is solved.
    patches = [
-        "@//third_party:com_google_absl_f863b622fe13612433fdf43f76547d5edda0c93001.diff"
+        "@//third_party:com_google_absl_windows_patch.diff"
    ],
    patch_args = [
        "-p1",
    ],
-    strip_prefix = "abseil-cpp-20220623.1",
+    strip_prefix = "abseil-cpp-20230125.0",
-    sha256 = "91ac87d30cc6d79f9ab974c51874a704de9c2647c40f6932597329a282217ba8"
+    sha256 = "3ea49a7d97421b88a8c48a0de16c16048e17725c7ec0f1d3ea2683a2a75adc21"
 )
 http_archive(
@@ -56,6 +54,76 @@ load("@rules_foreign_cc//:workspace_definitions.bzl", "rules_foreign_cc_dependen
 rules_foreign_cc_dependencies()
 http_archive(
    name = "com_google_protobuf",
    sha256 = "87407cd28e7a9c95d9f61a098a53cf031109d451a7763e7dd1253abf8b4df422",
    strip_prefix = "protobuf-3.19.1",
    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
    patches = [
        "@//third_party:com_google_protobuf_fixes.diff"
    ],
    patch_args = [
        "-p1",
    ],
 )
 # Load Zlib before initializing TensorFlow and the iOS build rules to guarantee
 # that the target @zlib//:mini_zlib is available
 http_archive(
    name = "zlib",
    build_file = "@//third_party:zlib.BUILD",
    sha256 = "c3e5e9fdd5004dcb542feda5ee4f0ff0744628baf8ed2dd5d66f8ca1197cb1a1",
    strip_prefix = "zlib-1.2.11",
    urls = [
        "http://mirror.bazel.build/zlib.net/fossils/zlib-1.2.11.tar.gz",
        "http://zlib.net/fossils/zlib-1.2.11.tar.gz",  # 2017-01-15
    ],
    patches = [
        "@//third_party:zlib.diff",
    ],
    patch_args = [
        "-p1",
    ],
 )
 # iOS basic build deps.
 http_archive(
    name = "build_bazel_rules_apple",
    sha256 = "3e2c7ae0ddd181c4053b6491dad1d01ae29011bc322ca87eea45957c76d3a0c3",
    url = "https://github.com/bazelbuild/rules_apple/releases/download/2.1.0/rules_apple.2.1.0.tar.gz",
    patches = [
        # Bypass checking ios unit test runner when building MP ios applications.
        "@//third_party:build_bazel_rules_apple_bypass_test_runner_check.diff"
    ],
    patch_args = [
        "-p1",
    ],
 )
 load(
    "@build_bazel_rules_apple//apple:repositories.bzl",
    "apple_rules_dependencies",
 )
 apple_rules_dependencies()
 load(
    "@build_bazel_rules_swift//swift:repositories.bzl",
    "swift_rules_dependencies",
 )
 swift_rules_dependencies()
 load(
    "@build_bazel_rules_swift//swift:extras.bzl",
    "swift_rules_extra_dependencies",
 )
 swift_rules_extra_dependencies()
 load(
    "@build_bazel_apple_support//lib:repositories.bzl",
    "apple_support_dependencies",
 )
 apple_support_dependencies()
 # This is used to select all contents of the archives for CMake-based packages to give CMake access to them.
 all_content = """filegroup(name = "all", srcs = glob(["**"]), visibility = ["//visibility:public"])"""
@@ -135,27 +203,18 @@ http_archive(
    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
 )
-http_archive(
+load("@//third_party/flatbuffers:workspace.bzl", flatbuffers = "repo")
    name = "com_google_protobuf",
    sha256 = "87407cd28e7a9c95d9f61a098a53cf031109d451a7763e7dd1253abf8b4df422",
    strip_prefix = "protobuf-3.19.1",
    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
    patches = [
        "@//third_party:com_google_protobuf_fixes.diff"
    ],
    patch_args = [
        "-p1",
    ],
 )
 load("//third_party/flatbuffers:workspace.bzl", flatbuffers = "repo")
 flatbuffers()
 http_archive(
    name = "com_google_audio_tools",
-    strip_prefix = "multichannel-audio-tools-master",
+    strip_prefix = "multichannel-audio-tools-1f6b1319f13282eda6ff1317be13de67f4723860",
-    urls = ["https://github.com/google/multichannel-audio-tools/archive/master.zip"],
+    urls = ["https://github.com/google/multichannel-audio-tools/archive/1f6b1319f13282eda6ff1317be13de67f4723860.zip"],
    sha256 = "fe346e1aee4f5069c4cbccb88706a9a2b2b4cf98aeb91ec1319be77e07dd7435",
    repo_mapping = {"@com_github_glog_glog" : "@com_github_glog_glog_no_gflags"},
    # TODO: Fix this in AudioTools directly
    patches = ["@//third_party:com_google_audio_tools_fixes.diff"],
    patch_args = ["-p1"]
 )
 http_archive(
@@ -174,7 +233,7 @@ http_archive(
        "https://github.com/google/sentencepiece/archive/1.0.0.zip",
    ],
    patches = [
-        "//third_party:com_google_sentencepiece_no_gflag_no_gtest.diff",
+        "@//third_party:com_google_sentencepiece_no_gflag_no_gtest.diff",
    ],
    patch_args = ["-p1"],
    repo_mapping = {"@com_google_glog" : "@com_github_glog_glog_no_gflags"},
@@ -188,8 +247,8 @@ http_archive(
        "https://github.com/tensorflow/text/archive/v2.2.0.zip",
    ],
    patches = [
-        "//third_party:tensorflow_text_remove_tf_deps.diff",
+        "@//third_party:tensorflow_text_remove_tf_deps.diff",
-        "//third_party:tensorflow_text_a0f49e63.diff",
+        "@//third_party:tensorflow_text_a0f49e63.diff",
    ],
    patch_args = ["-p1"],
    repo_mapping = {"@com_google_re2": "@com_googlesource_code_re2"},
@@ -271,7 +330,7 @@ new_local_repository(
    # For local MacOS builds, the path should point to an opencv@3 installation.
    # If you edit the path here, you will also need to update the corresponding
    # prefix in "opencv_macos.BUILD".
-    path = "/usr/local",
+    path = "/usr/local",  # e.g. /usr/local/Cellar for HomeBrew
 )
 new_local_repository(
@@ -320,63 +379,6 @@ http_archive(
    ],
 )
 # Load Zlib before initializing TensorFlow and the iOS build rules to guarantee
 # that the target @zlib//:mini_zlib is available
 http_archive(
    name = "zlib",
    build_file = "//third_party:zlib.BUILD",
    sha256 = "c3e5e9fdd5004dcb542feda5ee4f0ff0744628baf8ed2dd5d66f8ca1197cb1a1",
    strip_prefix = "zlib-1.2.11",
    urls = [
        "http://mirror.bazel.build/zlib.net/fossils/zlib-1.2.11.tar.gz",
        "http://zlib.net/fossils/zlib-1.2.11.tar.gz",  # 2017-01-15
    ],
    patches = [
        "@//third_party:zlib.diff",
    ],
    patch_args = [
        "-p1",
    ],
 )
 # iOS basic build deps.
 http_archive(
    name = "build_bazel_rules_apple",
    sha256 = "f94e6dddf74739ef5cb30f000e13a2a613f6ebfa5e63588305a71fce8a8a9911",
    url = "https://github.com/bazelbuild/rules_apple/releases/download/1.1.3/rules_apple.1.1.3.tar.gz",
    patches = [
        # Bypass checking ios unit test runner when building MP ios applications.
        "@//third_party:build_bazel_rules_apple_bypass_test_runner_check.diff"
    ],
    patch_args = [
        "-p1",
    ],
 )
 load(
    "@build_bazel_rules_apple//apple:repositories.bzl",
    "apple_rules_dependencies",
 )
 apple_rules_dependencies()
 load(
    "@build_bazel_rules_swift//swift:repositories.bzl",
    "swift_rules_dependencies",
 )
 swift_rules_dependencies()
 load(
    "@build_bazel_rules_swift//swift:extras.bzl",
    "swift_rules_extra_dependencies",
 )
 swift_rules_extra_dependencies()
 load(
    "@build_bazel_apple_support//lib:repositories.bzl",
    "apple_support_dependencies",
 )
 apple_support_dependencies()
 # More iOS deps.
 http_archive(
@@ -456,9 +458,9 @@ http_archive(
 )
 # TensorFlow repo should always go after the other external dependencies.
-# TF on 2022-08-10.
+# TF on 2023-03-08.
-_TENSORFLOW_GIT_COMMIT = "af1d5bc4fbb66d9e6cc1cf89503014a99233583b"
+_TENSORFLOW_GIT_COMMIT = "24f7ee636d62e1f8d8330357f8bbd65956dfb84d"
-_TENSORFLOW_SHA256 = "f85a5443264fc58a12d136ca6a30774b5bc25ceaf7d114d97f252351b3c3a2cb"
+_TENSORFLOW_SHA256 = "7f8a96dd99215c0cdc77230d3dbce43e60102b64a89203ad04aa09b0a187a4bd"
 http_archive(
    name = "org_tensorflow",
    urls = [
@@ -500,8 +502,8 @@ cc_crosstool(name = "crosstool")
 # Node dependencies
 http_archive(
    name = "build_bazel_rules_nodejs",
-    sha256 = "5aae76dced38f784b58d9776e4ab12278bc156a9ed2b1d9fcd3e39921dc88fda",
+    sha256 = "94070eff79305be05b7699207fbac5d2608054dd53e6109f7d00d923919ff45a",
-    urls = ["https://github.com/bazelbuild/rules_nodejs/releases/download/5.7.1/rules_nodejs-5.7.1.tar.gz"],
+    urls = ["https://github.com/bazelbuild/rules_nodejs/releases/download/5.8.2/rules_nodejs-5.8.2.tar.gz"],
 )
 load("@build_bazel_rules_nodejs//:repositories.bzl", "build_bazel_rules_nodejs_dependencies")
@@ -512,8 +514,8 @@ load("@build_bazel_rules_nodejs//:index.bzl", "node_repositories", "yarn_install
 node_repositories()
 yarn_install(
    name = "npm",
-    package_json = "//:package.json",
+    package_json = "@//:package.json",
-    yarn_lock = "//:yarn.lock",
+    yarn_lock = "@//:yarn.lock",
 )
 # Protobuf for Node dependencies
@@ -539,8 +541,48 @@ load("@rules_proto//proto:repositories.bzl", "rules_proto_dependencies", "rules_
 rules_proto_dependencies()
 rules_proto_toolchains()
-load("//third_party:external_files.bzl", "external_files")
+load("@//third_party:external_files.bzl", "external_files")
 external_files()
-load("//third_party:wasm_files.bzl", "wasm_files")
+load("@//third_party:wasm_files.bzl", "wasm_files")
 wasm_files()
 # Halide
 new_local_repository(
    name = "halide",
    build_file = "@//third_party/halide:BUILD.bazel",
    path = "third_party/halide"
 )
 http_archive(
    name = "linux_halide",
    sha256 = "d290fadf3f358c94aacf43c883de6468bb98883e26116920afd491ec0e440cd2",
    strip_prefix = "Halide-15.0.1-x86-64-linux",
    urls = ["https://github.com/halide/Halide/releases/download/v15.0.1/Halide-15.0.1-x86-64-linux-4c63f1befa1063184c5982b11b6a2cc17d4e5815.tar.gz"],
    build_file = "@//third_party:halide.BUILD",
 )
 http_archive(
    name = "macos_x86_64_halide",
    sha256 = "48ff073ac1aee5c4aca941a4f043cac64b38ba236cdca12567e09d803594a61c",
    strip_prefix = "Halide-15.0.1-x86-64-osx",
    urls = ["https://github.com/halide/Halide/releases/download/v15.0.1/Halide-15.0.1-x86-64-osx-4c63f1befa1063184c5982b11b6a2cc17d4e5815.tar.gz"],
    build_file = "@//third_party:halide.BUILD",
 )
 http_archive(
    name = "macos_arm_64_halide",
    sha256 = "db5d20d75fa7463490fcbc79c89f0abec9c23991f787c8e3e831fff411d5395c",
    strip_prefix = "Halide-15.0.1-arm-64-osx",
    urls = ["https://github.com/halide/Halide/releases/download/v15.0.1/Halide-15.0.1-arm-64-osx-4c63f1befa1063184c5982b11b6a2cc17d4e5815.tar.gz"],
    build_file = "@//third_party:halide.BUILD",
 )
 http_archive(
    name = "windows_halide",
    sha256 = "61fd049bd75ee918ac6c30d0693aac6048f63f8d1fc4db31001573e58eae8dae",
    strip_prefix = "Halide-15.0.1-x86-64-windows",
    urls = ["https://github.com/halide/Halide/releases/download/v15.0.1/Halide-15.0.1-x86-64-windows-4c63f1befa1063184c5982b11b6a2cc17d4e5815.zip"],
    build_file = "@//third_party:halide.BUILD",
 )
@@ -4,12 +4,10 @@ py_binary(
    name = "build_py_api_docs",
    srcs = ["build_py_api_docs.py"],
    deps = [
        "//mediapipe",
        "//third_party/py/absl:app",
        "//third_party/py/absl/flags",
-        "//third_party/py/tensorflow_docs",
+        "//third_party/py/mediapipe",
        "//third_party/py/tensorflow_docs/api_generator:generate_lib",
        "//third_party/py/tensorflow_docs/api_generator:public_api",
    ],
 )
@@ -18,11 +16,11 @@ py_binary(
    srcs = ["build_java_api_docs.py"],
    data = [
        "//third_party/android/sdk:api/26.txt",
-        "//third_party/java/doclava/current:doclava.jar",
+        "//third_party/java/doclava:doclet.jar",
        "//third_party/java/jsilver:jsilver_jar",
    ],
    env = {
-        "DOCLAVA_JAR": "$(location //third_party/java/doclava/current:doclava.jar)",
+        "DOCLAVA_JAR": "$(location //third_party/java/doclava:doclet.jar)",
        "JSILVER_JAR": "$(location //third_party/java/jsilver:jsilver_jar)",
    },
    deps = [
@@ -0,0 +1,13 @@
 <html lang="en">
 <head>
    <meta charset="utf-8"/>
    <meta http-equiv="refresh" content="0;url={{ page.target }}"/>
    <link rel="canonical" href="{{ page.target }}"/>
    <title>Redirecting</title>
 </head>
 <body>
    <p>This page now lives on https://developers.google.com/mediapipe/. If you aren't automatically
      redirected, follow this
    <a href="{{ page.target }}">link</a>.</p>
 </body>
 </html>
@@ -0,0 +1,704 @@
 ---
 layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/graphs_cpp
 title: Building Graphs in C++
 parent: Graphs
 nav_order: 1
 ---
 # Building Graphs in C++
 {: .no_toc }
 1. TOC
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 C++ graph builder is a powerful tool for:
 *   Building complex graphs
 *   Parametrizing graphs (e.g. setting a delegate on `InferenceCalculator`,
    enabling/disabling parts of the graph)
 *   Deduplicating graphs (e.g. instead of CPU and GPU dedicated graphs in pbtxt
    you can have a single code that constructs required graphs, sharing as much
    as possible)
 *   Supporting optional graph inputs/outputs
 *   Customizing graphs per platform
 ## Basic Usage
 Let's see how C++ graph builder can be used for a simple graph:
 ```proto
 # Graph inputs.
 input_stream: "input_tensors"
 input_side_packet: "model"
 # Graph outputs.
 output_stream: "output_tensors"
 node {
  calculator: "InferenceCalculator"
  input_stream: "TENSORS:input_tensors"
  input_side_packet: "MODEL:model"
  output_stream: "TENSORS:output_tensors"
  node_options: {
    [type.googleapis.com/mediapipe.InferenceCalculatorOptions] {
      # Requesting GPU delegate.
      delegate { gpu {} }
    }
  }
 }
 ```
 Function to build the above `CalculatorGraphConfig` may look like:
 ```c++
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Graph inputs.
  Stream<std::vector<Tensor>> input_tensors =
      graph.In(0).SetName("input_tensors").Cast<std::vector<Tensor>>();
  SidePacket<TfLiteModelPtr> model =
      graph.SideIn(0).SetName("model").Cast<TfLiteModelPtr>();
  auto& inference_node = graph.AddNode("InferenceCalculator");
  auto& inference_opts =
      inference_node.GetOptions<InferenceCalculatorOptions>();
  // Requesting GPU delegate.
  inference_opts.mutable_delegate()->mutable_gpu();
  input_tensors.ConnectTo(inference_node.In("TENSORS"));
  model.ConnectTo(inference_node.SideIn("MODEL"));
  Stream<std::vector<Tensor>> output_tensors =
      inference_node.Out("TENSORS").Cast<std::vector<Tensor>>();
  // Graph outputs.
  output_tensors.SetName("output_tensors").ConnectTo(graph.Out(0));
  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
 }
 ```
 Short summary:
 *   Use `Graph::In/SideIn` to get graph inputs as `Stream/SidePacket`
 *   Use `Node::Out/SideOut` to get node outputs as `Stream/SidePacket`
 *   Use `Stream/SidePacket::ConnectTo` to connect streams and side packets to
    node inputs (`Node::In/SideIn`) and graph outputs (`Graph::Out/SideOut`)
    *   There's a "shortcut" operator `>>` that you can use instead of
        `ConnectTo` function (E.g. `x >> node.In("IN")`).
 *   `Stream/SidePacket::Cast` is used to cast stream or side packet of `AnyType`
    (E.g. `Stream<AnyType> in = graph.In(0);`) to a particular type
    *   Using actual types instead of `AnyType` sets you on a better path for
        unleashing graph builder capabilities and improving your graphs
        readability.
 ## Advanced Usage
 ### Utility Functions
 Let's extract inference construction code into a dedicated utility function to
 help for readability and code reuse:
 ```c++
 // Updates graph to run inference.
 Stream<std::vector<Tensor>> RunInference(
    Stream<std::vector<Tensor>> tensors, SidePacket<TfLiteModelPtr> model,
    const InferenceCalculatorOptions::Delegate& delegate, Graph& graph) {
  auto& inference_node = graph.AddNode("InferenceCalculator");
  auto& inference_opts =
      inference_node.GetOptions<InferenceCalculatorOptions>();
  *inference_opts.mutable_delegate() = delegate;
  tensors.ConnectTo(inference_node.In("TENSORS"));
  model.ConnectTo(inference_node.SideIn("MODEL"));
  return inference_node.Out("TENSORS").Cast<std::vector<Tensor>>();
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Graph inputs.
  Stream<std::vector<Tensor>> input_tensors =
      graph.In(0).SetName("input_tensors").Cast<std::vector<Tensor>>();
  SidePacket<TfLiteModelPtr> model =
      graph.SideIn(0).SetName("model").Cast<TfLiteModelPtr>();
  InferenceCalculatorOptions::Delegate delegate;
  delegate.mutable_gpu();
  Stream<std::vector<Tensor>> output_tensors =
      RunInference(input_tensors, model, delegate, graph);
  // Graph outputs.
  output_tensors.SetName("output_tensors").ConnectTo(graph.Out(0));
  return graph.GetConfig();
 }
 ```
 As a result, `RunInference` provides a clear interface stating what are the
 inputs/outputs and their types.
 It can be easily reused, e.g. it's only a few lines if you want to run an extra
 model inference:
 ```c++
  // Run first inference.
  Stream<std::vector<Tensor>> output_tensors =
      RunInference(input_tensors, model, delegate, graph);
  // Run second inference on the output of the first one.
  Stream<std::vector<Tensor>> extra_output_tensors =
      RunInference(output_tensors, extra_model, delegate, graph);
 ```
 And you don't need to duplicate names and tags (`InferenceCalculator`,
 `TENSORS`, `MODEL`) or introduce dedicated constants here and there - those
 details are localized to `RunInference` function.
 Tip: extracting `RunInference` and similar functions to dedicated modules (e.g.
 inference.h/cc which depends on the inference calculator) enables reuse in
 graphs construction code and helps automatically pull in calculator dependencies
 (e.g. no need to manually add `:inference_calculator` dep, just let your IDE
 include `inference.h` and build cleaner pull in corresponding dependency).
 ### Utility Classes
 And surely, it's not only about functions, in some cases it's beneficial to
 introduce utility classes which can help making your graph construction code
 more readable and less error prone.
 MediaPipe offers `PassThroughCalculator` calculator, which is simply passing
 through its inputs:
 ```
 input_stream: "float_value"
 input_stream: "int_value"
 input_stream: "bool_value"
 output_stream: "passed_float_value"
 output_stream: "passed_int_value"
 output_stream: "passed_bool_value"
 node {
  calculator: "PassThroughCalculator"
  input_stream: "float_value"
  input_stream: "int_value"
  input_stream: "bool_value"
  # The order must be the same as for inputs (or you can use explicit indexes)
  output_stream: "passed_float_value"
  output_stream: "passed_int_value"
  output_stream: "passed_bool_value"
 }
 ```
 Let's see the straightforward C++ construction code to create the above graph:
 ```c++
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Graph inputs.
  Stream<float> float_value = graph.In(0).SetName("float_value").Cast<float>();
  Stream<int> int_value = graph.In(1).SetName("int_value").Cast<int>();
  Stream<bool> bool_value = graph.In(2).SetName("bool_value").Cast<bool>();
  auto& pass_node = graph.AddNode("PassThroughCalculator");
  float_value.ConnectTo(pass_node.In("")[0]);
  int_value.ConnectTo(pass_node.In("")[1]);
  bool_value.ConnectTo(pass_node.In("")[2]);
  Stream<float> passed_float_value = pass_node.Out("")[0].Cast<float>();
  Stream<int> passed_int_value = pass_node.Out("")[1].Cast<int>();
  Stream<bool> passed_bool_value = pass_node.Out("")[2].Cast<bool>();
  // Graph outputs.
  passed_float_value.SetName("passed_float_value").ConnectTo(graph.Out(0));
  passed_int_value.SetName("passed_int_value").ConnectTo(graph.Out(1));
  passed_bool_value.SetName("passed_bool_value").ConnectTo(graph.Out(2));
  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
 }
 ```
 While `pbtxt` representation maybe error prone (when we have many inputs to pass
 through), C++ code looks even worse: repeated empty tags and `Cast` calls. Let's
 see how we can do better by introducing a `PassThroughNodeBuilder`:
 ```c++
 class PassThroughNodeBuilder {
 public:
  explicit PassThroughNodeBuilder(Graph& graph)
      : node_(graph.AddNode("PassThroughCalculator")) {}
  template <typename T>
  Stream<T> PassThrough(Stream<T> stream) {
    stream.ConnectTo(node_.In(index_));
    return node_.Out(index_++).Cast<T>();
  }
 private:
  int index_ = 0;
  GenericNode& node_;
 };
 ```
 And now graph construction code can look like:
 ```c++
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Graph inputs.
  Stream<float> float_value = graph.In(0).SetName("float_value").Cast<float>();
  Stream<int> int_value = graph.In(1).SetName("int_value").Cast<int>();
  Stream<bool> bool_value = graph.In(2).SetName("bool_value").Cast<bool>();
  PassThroughNodeBuilder pass_node_builder(graph);
  Stream<float> passed_float_value = pass_node_builder.PassThrough(float_value);
  Stream<int> passed_int_value = pass_node_builder.PassThrough(int_value);
  Stream<bool> passed_bool_value = pass_node_builder.PassThrough(bool_value);
  // Graph outputs.
  passed_float_value.SetName("passed_float_value").ConnectTo(graph.Out(0));
  passed_int_value.SetName("passed_int_value").ConnectTo(graph.Out(1));
  passed_bool_value.SetName("passed_bool_value").ConnectTo(graph.Out(2));
  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
 }
 ```
 Now you can't have incorrect order or index in your pass through construction
 code and save some typing by guessing the type for `Cast` from the `PassThrough`
 input.
 Tip: the same as for the `RunInference` function, extracting
 `PassThroughNodeBuilder` and similar utility classes into dedicated modules
 enables reuse in graph construction code and helps to automatically pull in the
 corresponding calculator dependencies.
 ## Dos and Don'ts
 ### Define graph inputs at the very beginning if possible
 ```c++ {.bad}
 Stream<D> RunSomething(Stream<A> a, Stream<B> b, Graph& graph) {
  Stream<C> c = graph.In(2).SetName("c").Cast<C>();  // Bad.
  // ...
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  Stream<A> a = graph.In(0).SetName("a").Cast<A>();
  // 10/100/N lines of code.
  Stream<B> b = graph.In(1).SetName("b").Cast<B>()  // Bad.
  Stream<D> d = RunSomething(a, b, graph);
  // ...
  return graph.GetConfig();
 }
 ```
 In the above code:
 *   It can be hard to guess how many inputs you have in the graph.
 *   Can be error prone overall and hard to maintain in future (e.g. is it a
    correct index? name? what if some inputs are removed or made optional?
    etc.).
 *   `RunSomething` reuse is limited because other graphs may have different
    inputs
 Instead, define your graph inputs at the very beginning of your graph builder:
 ```c++ {.good}
 Stream<D> RunSomething(Stream<A> a, Stream<B> b, Stream<C> c, Graph& graph) {
  // ...
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).SetName("a").Cast<A>();
  Stream<B> b = graph.In(1).SetName("b").Cast<B>();
  Stream<C> c = graph.In(2).SetName("c").Cast<C>();
  // 10/100/N lines of code.
  Stream<D> d = RunSomething(a, b, c, graph);
  // ...
  return graph.GetConfig();
 }
 ```
 Use `std::optional` if you have an input stream or side packet that is not
 always defined and put it at the very beginning:
 ```c++ {.good}
 std::optional<Stream<A>> a;
 if (needs_a) {
  a = graph.In(0).SetName(a).Cast<A>();
 }
 ```
 Note: of course, there can be exceptions - for example, there can be a use case
 where calling `RunSomething1(..., graph)`, ..., `RunSomethingN(..., graph)` is
 **intended to add new inputs**, so afterwards you can iterate over them and feed
 only added inputs into the graph. However, in any case, try to make it easy for
 readers to find out what graph inputs it has or may have.
 ### Define graph outputs at the very end
 ```c++ {.bad}
 void RunSomething(Stream<Input> input, Graph& graph) {
  // ...
  node.Out("OUTPUT_F")
      .SetName("output_f").ConnectTo(graph.Out(2));  // Bad.
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // 10/100/N lines of code.
  node.Out("OUTPUT_D")
      .SetName("output_d").ConnectTo(graph.Out(0));  // Bad.
  // 10/100/N lines of code.
  node.Out("OUTPUT_E")
      .SetName("output_e").ConnectTo(graph.Out(1));  // Bad.
  // 10/100/N lines of code.
  RunSomething(input, graph);
  // ...
  return graph.GetConfig();
 }
 ```
 In the above code:
 *   It can be hard to guess how many outputs you have in the graph.
 *   Can be error prone overall and hard to maintain in future (e.g. is it a
    correct index? name? what if some outpus are removed or made optional?
    etc.).
 *   `RunSomething` reuse is limited as other graphs may have different outputs
 Instead, define your graph outputs at the very end of your graph builder:
 ```c++ {.good}
 Stream<F> RunSomething(Stream<Input> input, Graph& graph) {
  // ...
  return node.Out("OUTPUT_F").Cast<F>();
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // 10/100/N lines of code.
  Stream<D> d = node.Out("OUTPUT_D").Cast<D>();
  // 10/100/N lines of code.
  Stream<E> e = node.Out("OUTPUT_E").Cast<E>();
  // 10/100/N lines of code.
  Stream<F> f = RunSomething(input, graph);
  // ...
  // Outputs.
  d.SetName("output_d").ConnectTo(graph.Out(0));
  e.SetName("output_e").ConnectTo(graph.Out(1));
  f.SetName("output_f").ConnectTo(graph.Out(2));
  return graph.GetConfig();
 }
 ```
 ### Keep nodes decoupled from each other
 In MediaPipe, packet streams and side packets are as meaningful as processing
 nodes. And any node input requirements and output products are expressed clearly
 and independently in terms of the streams and side packets it consumes and
 produces.
 ```c++ {.bad}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  auto& node1 = graph.AddNode("Calculator1");
  a.ConnectTo(node1.In("INPUT"));
  auto& node2 = graph.AddNode("Calculator2");
  node1.Out("OUTPUT").ConnectTo(node2.In("INPUT"));  // Bad.
  auto& node3 = graph.AddNode("Calculator3");
  node1.Out("OUTPUT").ConnectTo(node3.In("INPUT_B"));  // Bad.
  node2.Out("OUTPUT").ConnectTo(node3.In("INPUT_C"));  // Bad.
  auto& node4 = graph.AddNode("Calculator4");
  node1.Out("OUTPUT").ConnectTo(node4.In("INPUT_B"));  // Bad.
  node2.Out("OUTPUT").ConnectTo(node4.In("INPUT_C"));  // Bad.
  node3.Out("OUTPUT").ConnectTo(node4.In("INPUT_D"));  // Bad.
  // Outputs.
  node1.Out("OUTPUT").SetName("b").ConnectTo(graph.Out(0));  // Bad.
  node2.Out("OUTPUT").SetName("c").ConnectTo(graph.Out(1));  // Bad.
  node3.Out("OUTPUT").SetName("d").ConnectTo(graph.Out(2));  // Bad.
  node4.Out("OUTPUT").SetName("e").ConnectTo(graph.Out(3));  // Bad.
  return graph.GetConfig();
 }
 ```
 In the above code:
 *   Nodes are coupled to each other, e.g. `node4` knows where its inputs are
    coming from (`node1`, `node2`, `node3`) and it complicates refactoring,
    maintenance and code reuse
    *   Such usage pattern is a downgrade from proto representation, where nodes
        are decoupled by default.
 *   `node#.Out("OUTPUT")` calls are duplicated and readability suffers as you
    could use cleaner names instead and also provide an actual type.
 So, to fix the above issues you can write the following graph construction code:
 ```c++ {.good}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  // `node1` usage is limited to 3 lines below.
  auto& node1 = graph.AddNode("Calculator1");
  a.ConnectTo(node1.In("INPUT"));
  Stream<B> b = node1.Out("OUTPUT").Cast<B>();
  // `node2` usage is limited to 3 lines below.
  auto& node2 = graph.AddNode("Calculator2");
  b.ConnectTo(node2.In("INPUT"));
  Stream<C> c = node2.Out("OUTPUT").Cast<C>();
  // `node3` usage is limited to 4 lines below.
  auto& node3 = graph.AddNode("Calculator3");
  b.ConnectTo(node3.In("INPUT_B"));
  c.ConnectTo(node3.In("INPUT_C"));
  Stream<D> d = node3.Out("OUTPUT").Cast<D>();
  // `node4` usage is limited to 5 lines below.
  auto& node4 = graph.AddNode("Calculator4");
  b.ConnectTo(node4.In("INPUT_B"));
  c.ConnectTo(node4.In("INPUT_C"));
  d.ConnectTo(node4.In("INPUT_D"));
  Stream<E> e = node4.Out("OUTPUT").Cast<E>();
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
 Now, if needed, you can easily remove `node1` and make `b` a graph input and no
 updates are needed to `node2`, `node3`, `node4` (same as in proto representation
 by the way), because they are decoupled from each other.
 Overall, the above code replicates the proto graph more closely:
 ```proto
 input_stream: "a"
 node {
  calculator: "Calculator1"
  input_stream: "INPUT:a"
  output_stream: "OUTPUT:b"
 }
 node {
  calculator: "Calculator2"
  input_stream: "INPUT:b"
  output_stream: "OUTPUT:C"
 }
 node {
  calculator: "Calculator3"
  input_stream: "INPUT_B:b"
  input_stream: "INPUT_C:c"
  output_stream: "OUTPUT:d"
 }
 node {
  calculator: "Calculator4"
  input_stream: "INPUT_B:b"
  input_stream: "INPUT_C:c"
  input_stream: "INPUT_D:d"
  output_stream: "OUTPUT:e"
 }
 output_stream: "b"
 output_stream: "c"
 output_stream: "d"
 output_stream: "e"
 ```
 On top of that, now you can extract utility functions for further reuse in other graphs:
 ```c++ {.good}
 Stream<B> RunCalculator1(Stream<A> a, Graph& graph) {
  auto& node = graph.AddNode("Calculator1");
  a.ConnectTo(node.In("INPUT"));
  return node.Out("OUTPUT").Cast<B>();
 }
 Stream<C> RunCalculator2(Stream<B> b, Graph& graph) {
  auto& node = graph.AddNode("Calculator2");
  b.ConnectTo(node.In("INPUT"));
  return node.Out("OUTPUT").Cast<C>();
 }
 Stream<D> RunCalculator3(Stream<B> b, Stream<C> c, Graph& graph) {
  auto& node = graph.AddNode("Calculator3");
  b.ConnectTo(node.In("INPUT_B"));
  c.ConnectTo(node.In("INPUT_C"));
  return node.Out("OUTPUT").Cast<D>();
 }
 Stream<E> RunCalculator4(Stream<B> b, Stream<C> c, Stream<D> d, Graph& graph) {
  auto& node = graph.AddNode("Calculator4");
  b.ConnectTo(node.In("INPUT_B"));
  c.ConnectTo(node.In("INPUT_C"));
  d.ConnectTo(node.In("INPUT_D"));
  return node.Out("OUTPUT").Cast<E>();
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  Stream<B> b = RunCalculator1(a, graph);
  Stream<C> c = RunCalculator2(b, graph);
  Stream<D> d = RunCalculator3(b, c, graph);
  Stream<E> e = RunCalculator4(b, c, d, graph);
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
 ### Separate nodes for better readability
 ```c++ {.bad}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  auto& node1 = graph.AddNode("Calculator1");
  a.ConnectTo(node1.In("INPUT"));
  Stream<B> b = node1.Out("OUTPUT").Cast<B>();
  auto& node2 = graph.AddNode("Calculator2");
  b.ConnectTo(node2.In("INPUT"));
  Stream<C> c = node2.Out("OUTPUT").Cast<C>();
  auto& node3 = graph.AddNode("Calculator3");
  b.ConnectTo(node3.In("INPUT_B"));
  c.ConnectTo(node3.In("INPUT_C"));
  Stream<D> d = node3.Out("OUTPUT").Cast<D>();
  auto& node4 = graph.AddNode("Calculator4");
  b.ConnectTo(node4.In("INPUT_B"));
  c.ConnectTo(node4.In("INPUT_C"));
  d.ConnectTo(node4.In("INPUT_D"));
  Stream<E> e = node4.Out("OUTPUT").Cast<E>();
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
 In the above code, it can be hard to grasp the idea where each node begins and
 ends. To improve this and help your code readers, you can simply have blank
 lines before and after each node:
 ```c++ {.good}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  auto& node1 = graph.AddNode("Calculator1");
  a.ConnectTo(node1.In("INPUT"));
  Stream<B> b = node1.Out("OUTPUT").Cast<B>();
  auto& node2 = graph.AddNode("Calculator2");
  b.ConnectTo(node2.In("INPUT"));
  Stream<C> c = node2.Out("OUTPUT").Cast<C>();
  auto& node3 = graph.AddNode("Calculator3");
  b.ConnectTo(node3.In("INPUT_B"));
  c.ConnectTo(node3.In("INPUT_C"));
  Stream<D> d = node3.Out("OUTPUT").Cast<D>();
  auto& node4 = graph.AddNode("Calculator4");
  b.ConnectTo(node4.In("INPUT_B"));
  c.ConnectTo(node4.In("INPUT_C"));
  d.ConnectTo(node4.In("INPUT_D"));
  Stream<E> e = node4.Out("OUTPUT").Cast<E>();
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
 Also, the above representation matches `CalculatorGraphConfig` proto
 representation better.
 If you extract nodes into utility functions, they are scoped within functions
 already and it's clear where they begin and end, so it's completely fine to
 have:
 ```c++ {.good}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  Stream<B> b = RunCalculator1(a, graph);
  Stream<C> c = RunCalculator2(b, graph);
  Stream<D> d = RunCalculator3(b, c, graph);
  Stream<E> e = RunCalculator4(b, c, d, graph);
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/calculators
 title: Calculators
 parent: Framework Concepts
 nav_order: 1
@@ -12,6 +13,12 @@ nav_order: 1
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 Each calculator is a node of a graph. We describe how to create a new
 calculator, how to initialize a calculator, how to perform its calculations,
 input and output streams, timestamps, and options. Each node in the graph is
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/overview
 title: Framework Concepts
 nav_order: 5
 has_children: true
@@ -13,6 +14,12 @@ has_toc: false
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## The basics
 ### Packet
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/gpu
 title: GPU
 parent: Framework Concepts
 nav_order: 5
@@ -12,6 +13,12 @@ nav_order: 5
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## Overview
 MediaPipe supports calculator nodes for GPU compute and rendering, and allows combining multiple GPU nodes, as well as mixing them with CPU based calculator nodes. There exist several GPU APIs on mobile platforms (eg, OpenGL ES, Metal and Vulkan). MediaPipe does not attempt to offer a single cross-API GPU abstraction. Individual nodes can be written using different APIs, allowing them to take advantage of platform specific features when needed.
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/graphs
 title: Graphs
 parent: Framework Concepts
 nav_order: 2
@@ -12,31 +13,37 @@ nav_order: 2
 {:toc}
 ---
-## GraphConfig
+**Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
-A `GraphConfig` is a specification that describes the topology and functionality
+----
-of a MediaPipe graph. In the specification, a node in the graph represents an
+
-instance of a particular calculator. All the necessary configurations of the
+## Graph
-node, such its type, inputs and outputs must be described in the specification.
+
-Description of the node can also include several optional fields, such as
+A `CalculatorGraphConfig` proto specifies the topology and functionality of a
 MediaPipe graph. Each `node` in the graph represents a particular calculator or
 subgraph, and specifies necessary configurations, such as registered
 calculator/subgraph type, inputs, outputs and optional fields, such as
 node-specific options, input policy and executor, discussed in
 [Synchronization](synchronization.md).
-`GraphConfig` has several other fields to configure the global graph-level
+`CalculatorGraphConfig` has several other fields to configure global graph-level
-settings, eg, graph executor configs, number of threads, and maximum queue size
+settings, e.g. graph executor configs, number of threads, and maximum queue size
 of input streams. Several graph-level settings are useful for tuning the
-performance of the graph on different platforms (eg, desktop v.s. mobile). For
+performance of the graph on different platforms (e.g., desktop v.s. mobile). For
 instance, on mobile, attaching a heavy model-inference calculator to a separate
 executor can improve the performance of a real-time application since this
 enables thread locality.
-Below is a trivial `GraphConfig` example where we have series of passthrough
+Below is a trivial `CalculatorGraphConfig` example where we have series of
-calculators :
+passthrough calculators :
 ```proto
 # This graph named main_pass_throughcals_nosubgraph.pbtxt contains 4
 # passthrough calculators.
 input_stream: "in"
 output_stream: "out"
 node {
    calculator: "PassThroughCalculator"
    input_stream: "in"
@@ -55,17 +62,46 @@ node {
 node {
    calculator: "PassThroughCalculator"
    input_stream: "out3"
-    output_stream: "out4"
+    output_stream: "out"
 }
 ```
 MediaPipe offers an alternative `C++` representation for complex graphs (e.g. ML pipelines, handling model metadata, optional nodes, etc.). The above graph may look like:
 ```c++
 CalculatorGraphConfig BuildGraphConfig() {
  Graph graph;
  // Graph inputs
  Stream<AnyType> in = graph.In(0).SetName("in");
  auto pass_through_fn = [](Stream<AnyType> in,
                            Graph& graph) -> Stream<AnyType> {
    auto& node = graph.AddNode("PassThroughCalculator");
    in.ConnectTo(node.In(0));
    return node.Out(0);
  };
  Stream<AnyType> out1 = pass_through_fn(in, graph);
  Stream<AnyType> out2 = pass_through_fn(out1, graph);
  Stream<AnyType> out3 = pass_through_fn(out2, graph);
  Stream<AnyType> out4 = pass_through_fn(out3, graph);
  // Graph outputs
  out4.SetName("out").ConnectTo(graph.Out(0));
  return graph.GetConfig();
 }
 ```
 See more details in [Building Graphs in C++](building_graphs_cpp.md)
 ## Subgraph
 To modularize a `CalculatorGraphConfig` into sub-modules and assist with re-use
 of perception solutions, a MediaPipe graph can be defined as a `Subgraph`. The
 public interface of a subgraph consists of a set of input and output streams
 similar to a calculator's public interface. The subgraph can then be included in
-an `CalculatorGraphConfig` as if it were a calculator. When a MediaPipe graph is
+a `CalculatorGraphConfig` as if it were a calculator. When a MediaPipe graph is
 loaded from a `CalculatorGraphConfig`, each subgraph node is replaced by the
 corresponding graph of calculators. As a result, the semantics and performance
 of the subgraph is identical to the corresponding graph of calculators.
@@ -151,7 +187,7 @@ protobuf specified for a MediaPipe calculator. These "graph options" can be
 specified where a graph is invoked, and used to populate calculator options and
 subgraph options within the graph.
-In a CalculatorGraphConfig, graph options can be specified for a subgraph
+In a `CalculatorGraphConfig`, graph options can be specified for a subgraph
 exactly like calculator options, as shown below:
 ```
@@ -178,7 +214,7 @@ node {
 }
 ```
-In a CalculatorGraphConfig, graph options can be accepted and used to populate
+In a `CalculatorGraphConfig`, graph options can be accepted and used to populate
 calculator options, as shown below:
 ```
@@ -188,7 +224,7 @@ graph_options: {
 node: {
  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:multi_backend_image"
+  input_stream: "IMAGE:image"
  node_options: {
    [type.googleapis.com/mediapipe.ImageToTensorCalculatorOptions] {
        keep_aspect_ratio: true
@@ -247,9 +283,9 @@ NOTE: The current approach is experimental and subject to change. We welcome
 your feedback.
 Please use the `CalculatorGraphTest.Cycle` unit test in
-`mediapipe/framework/calculator_graph_test.cc` as sample code. Shown
+`mediapipe/framework/calculator_graph_test.cc` as sample code. Shown below is
-below is the cyclic graph in the test. The `sum` output of the adder is the sum
+the cyclic graph in the test. The `sum` output of the adder is the sum of the
-of the integers generated by the integer source calculator.
+integers generated by the integer source calculator.
 ![a cyclic graph that adds a stream of integers](https://mediapipe.dev/images/cyclic_integer_sum_graph.svg "A cyclic graph")
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/packets
 title: Packets
 parent: Framework Concepts
 nav_order: 3
@@ -12,6 +13,12 @@ nav_order: 3
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 Calculators communicate by sending and receiving packets. Typically a single
 packet is sent along each input stream at each input timestamp. A packet can
 contain any kind of data, such as a single frame of video or a single integer
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/realtime_streams
 title: Real-time Streams
 parent: Framework Concepts
 nav_order: 6
@@ -12,6 +13,12 @@ nav_order: 6
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## Real-time timestamps
 MediaPipe calculator graphs are often used to process streams of video or audio
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/synchronization
 title: Synchronization
 parent: Framework Concepts
 nav_order: 4
@@ -12,6 +13,12 @@ nav_order: 4
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## Scheduling mechanics
 Data processing in a MediaPipe graph occurs inside processing nodes defined as
@@ -112,14 +119,14 @@ Warning: On the other hand, it is not guaranteed that an input packet will
 always be available for all streams.
 To explain how it works, we need to introduce the definition of a settled
-timestamp. We say that a timestamp in a stream is *settled* if it lower than the
+timestamp. We say that a timestamp in a stream is *settled* if it is lower than
-timestamp bound. In other words, a timestamp is settled for a stream once the
+the timestamp bound. In other words, a timestamp is settled for a stream once
-state of the input at that timestamp is irrevocably known: either there is a
+the state of the input at that timestamp is irrevocably known: either there is a
 packet, or there is the certainty that a packet with that timestamp will not
 arrive.
 Note: For this reason, MediaPipe also allows a stream producer to explicitly
-advance the timestamp bound farther that what the last packet implies, i.e. to
+advance the timestamp bound farther than what the last packet implies, i.e. to
 provide a tighter bound. This can allow the downstream nodes to settle their
 inputs sooner.
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/android
 title: MediaPipe on Android
 parent: Getting Started
 has_children: true
@@ -14,6 +15,12 @@ nav_order: 1
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 Please follow instructions below to build Android example apps in the supported
 MediaPipe [solutions](../solutions/solutions.md). To learn more about these
 example apps, start from [Hello World! on Android](./hello_world_android.md).
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/android_archive_library
 title: MediaPipe Android Archive
 parent: MediaPipe on Android
 grand_parent: Getting Started
@@ -13,6 +14,12 @@ nav_order: 3
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ***Experimental Only***
 The MediaPipe Android Archive (AAR) library is a convenient way to use MediaPipe
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/
 title: MediaPipe Android Solutions
 parent: MediaPipe on Android
 grand_parent: Getting Started
@@ -13,6 +14,12 @@ nav_order: 2
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 MediaPipe Android Solution APIs (currently in alpha) are available in:
 *   [MediaPipe Face Detection](../solutions/face_detection#android-solution-api)
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/
 title: Building MediaPipe Examples
 parent: Getting Started
 nav_exclude: true
@@ -12,6 +13,12 @@ nav_exclude: true
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ### Android
 Please see these [instructions](./android.md).
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/cpp
 title: MediaPipe in C++
 parent: Getting Started
 has_children: true
@@ -14,6 +15,12 @@ nav_order: 5
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 Please follow instructions below to build C++ command-line example apps in the
 supported MediaPipe [solutions](../solutions/solutions.md). To learn more about
 these example apps, start from [Hello World! in C++](./hello_world_cpp.md).
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/faq
 title: FAQ
 parent: Getting Started
 nav_order: 9
@@ -12,6 +13,12 @@ nav_order: 9
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ### How to convert ImageFrames and GpuBuffers
 The Calculators [`ImageFrameToGpuBufferCalculator`] and
@@ -46,7 +53,7 @@ calculators need only to be *thread-compatible* and not *thread-safe*.
 In order to enable one calculator to process multiple inputs in parallel, there
 are two possible approaches:
-1.  Define multiple calulator nodes and dispatch input packets to all nodes.
+1.  Define multiple calculator nodes and dispatch input packets to all nodes.
 2.  Make the calculator thread-safe and configure its [`max_in_flight`] setting.
 The first approach can be followed using the calculators designed to distribute
@@ -59,7 +66,7 @@ The second approach allows up to [`max_in_flight`] invocations of the
 packets from [`CalculatorBase::Process`] are automatically ordered by timestamp
 before they are passed along to downstream calculators.
-With either aproach, you must be aware that the calculator running in parallel
+With either approach, you must be aware that the calculator running in parallel
 cannot maintain internal state in the same way as a normal sequential
 calculator.
@@ -88,12 +95,12 @@ while the application is running:
 The first approach has the advantage of leveraging [`CalculatorGraphConfig`]
 processing tools such as "subgraphs". The second approach has the advantage of
 allowing active calculators and packets to remain in-flight while settings
-change. Mediapipe contributors are currently investigating alternative approaches
+change. MediaPipe contributors are currently investigating alternative approaches
 to achieve both of these advantages.
 ### How to process realtime input streams
-The mediapipe framework can be used to process data streams either online or
+The MediaPipe framework can be used to process data streams either online or
 offline. For offline processing, packets are pushed into the graph as soon as
 calculators are ready to process those packets. For online processing, one
 packet for each frame is pushed into the graph as that frame is recorded.
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/
 title: Getting Started
 nav_order: 2
 has_children: true
@@ -11,3 +12,9 @@ has_children: true
 1. TOC
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/gpu_support
 title: GPU Support
 parent: Getting Started
 nav_order: 7
@@ -12,6 +13,12 @@ nav_order: 7
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## OpenGL ES Support
 MediaPipe supports OpenGL ES up to version 3.2 on Android/Linux and up to ES 3.0
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/hello_world_android
 title: Hello World! on Android
 parent: MediaPipe on Android
 grand_parent: Getting Started
@@ -13,6 +14,12 @@ nav_order: 1
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## Introduction
 This codelab uses MediaPipe on an Android device.
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/hello_world_cpp
 title: Hello World! in C++
 parent: MediaPipe in C++
 grand_parent: Getting Started
@@ -13,6 +14,12 @@ nav_order: 1
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 1.  Ensure you have a working version of MediaPipe. See
    [installation instructions](./install.md).
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/hello_world_ios
 title: Hello World! on iOS
 parent: MediaPipe on iOS
 grand_parent: Getting Started
@@ -13,6 +14,12 @@ nav_order: 1
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## Introduction
 This codelab uses MediaPipe on an iOS device.
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/help
 title: Getting Help
 parent: Getting Started
 nav_order: 8
@@ -12,6 +13,12 @@ nav_order: 8
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## Technical questions
 For help with technical or algorithmic questions, visit
@@ -37,8 +44,8 @@ If you open a GitHub issue, here is our policy:
 - **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
 - **Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device**:
 - **Bazel version**:
- **Android Studio, NDK, SDK versions (if issue is related to building in mobile dev enviroment)**:
+- **Android Studio, NDK, SDK versions (if issue is related to building in mobile dev environment)**:
- **Xcode & Tulsi version (if issue is related to building in mobile dev enviroment)**:
+- **Xcode & Tulsi version (if issue is related to building in mobile dev environment)**:
 - **Exact steps to reproduce**:
 ### Describe the problem
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/install
 title: Installation
 parent: Getting Started
 nav_order: 6
@@ -12,6 +13,12 @@ nav_order: 6
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 Note: To interoperate with OpenCV, OpenCV 3.x to 4.1 are preferred. OpenCV
 2.x currently works but interoperability support may be deprecated in the
 future.
@@ -35,7 +42,7 @@ install --user six`.
    ```bash
    $ cd $HOME
-    $ git clone https://github.com/google/mediapipe.git
+    $ git clone --depth 1 https://github.com/google/mediapipe.git
    # Change directory into MediaPipe root directory
    $ cd mediapipe
@@ -67,6 +74,13 @@ install --user six`.
        libopencv-video-dev
    ```
    **Note**. On Debian 11/Ubuntu 21.04 where OpenCV 4.5 is installed with
    `libopencv-video-dev`, `libopencv-contrib-dev` should also be installed.
    ```bash
    $ sudo apt-get install -y libopencv-contrib-dev
    ```
    MediaPipe's [`opencv_linux.BUILD`] and [`WORKSPACE`] are already configured
    for OpenCV 2/3 and should work correctly on any architecture:
@@ -287,7 +301,7 @@ build issues.
 2.  Checkout MediaPipe repository.
    ```bash
-    $ git clone https://github.com/google/mediapipe.git
+    $ git clone --depth 1 https://github.com/google/mediapipe.git
    # Change directory into MediaPipe root directory
    $ cd mediapipe
@@ -416,7 +430,7 @@ build issues.
 3.  Checkout MediaPipe repository.
    ```bash
-    $ git clone https://github.com/google/mediapipe.git
+    $ git clone --depth 1 https://github.com/google/mediapipe.git
    $ cd mediapipe
    ```
@@ -569,7 +583,7 @@ next section.
    Option 1. Follow
    [the official Bazel documentation](https://docs.bazel.build/versions/master/install-windows.html)
-    to install Bazel 5.2.0 or higher.
+    to install Bazel 6.1.1 or higher.
    Option 2. Follow the official
    [Bazel documentation](https://docs.bazel.build/versions/master/install-bazelisk.html)
@@ -590,7 +604,7 @@ next section.
 7.  Checkout MediaPipe repository.
    ```
-    C:\Users\Username\mediapipe_repo> git clone https://github.com/google/mediapipe.git
+    C:\Users\Username\mediapipe_repo> git clone --depth 1 https://github.com/google/mediapipe.git
    # Change directory into MediaPipe root directory
    C:\Users\Username\mediapipe_repo> cd mediapipe
@@ -651,7 +665,7 @@ cameras. Alternatively, you use a video file as input.
 1.  Follow the
    [instruction](https://docs.microsoft.com/en-us/windows/wsl/install-win10) to
-    install Windows Sysystem for Linux (Ubuntu).
+    install Windows Subsystem for Linux (Ubuntu).
 2.  Install Windows ADB and start the ADB server in Windows.
@@ -680,7 +694,7 @@ cameras. Alternatively, you use a video file as input.
 6.  Checkout MediaPipe repository.
    ```bash
-    username@DESKTOP-TMVLBJ1:~$ git clone https://github.com/google/mediapipe.git
+    username@DESKTOP-TMVLBJ1:~$ git clone --depth 1 https://github.com/google/mediapipe.git
    username@DESKTOP-TMVLBJ1:~$ cd mediapipe
    ```
@@ -771,7 +785,7 @@ This will use a Docker image that will isolate mediapipe's installation from the
 2.  Build a docker image with tag "mediapipe".
    ```bash
-    $ git clone https://github.com/google/mediapipe.git
+    $ git clone --depth 1 https://github.com/google/mediapipe.git
    $ cd mediapipe
    $ docker build --tag=mediapipe .
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/ios
 title: MediaPipe on iOS
 parent: Getting Started
 has_children: true
@@ -14,6 +15,12 @@ nav_order: 2
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 Please follow instructions below to build iOS example apps in the supported
 MediaPipe [solutions](../solutions/solutions.md). To learn more about these
 example apps, start from, start from
@@ -91,6 +98,10 @@ allows you to make use of automatic provisioning (see later section).
    This will install `Tulsi.app` inside the `Applications` directory in your
    home directory.
    **Note**: Please ensure the `xcode_version` in the
    [`build_and_run.sh`](https://github.com/bazelbuild/tulsi/blob/b1d0108e6a93dbe8ab01529b2c607b6b651f0759/build_and_run.sh#L26)
    file in tulsi repo is the same version as installed in your system.
 2.  Open `mediapipe/Mediapipe.tulsiproj` using the Tulsi app.
    Tip: If Tulsi displays an error saying "Bazel could not be found", press the
@@ -109,8 +120,8 @@ allows you to make use of automatic provisioning (see later section).
 To install applications on an iOS device, you need a provisioning profile. There
 are two options:
-1.  Automatic provisioning. This allows you to build and install an app to your
+1.  Automatic provisioning. This allows you to build and install an app on your
-    personal device. The provisining profile is managed by Xcode, and has to be
+    personal device. The provisioning profile is managed by Xcode, and has to be
    updated often (it is valid for about a week).
 2.  Custom provisioning. This uses a provisioning profile associated with an
@@ -175,7 +186,7 @@ Profiles"`. If there are none, generate and download a profile on
 Note: if you had previously set up automatic provisioning, you should remove the
 `provisioning_profile.mobileprovision` symlink in each example's directory,
 since it will take precedence over the common one. You can also overwrite it
-with you own profile if you need a different profile for different apps.
+with your own profile if you need a different profile for different apps.
 1.  Open `mediapipe/examples/ios/bundle_id.bzl`, and change the
    `BUNDLE_ID_PREFIX` to a prefix associated with your provisioning profile.
@@ -192,7 +203,7 @@ Note: When you ask Xcode to run an app, by default it will use the Debug
 configuration. Some of our demos are computationally heavy; you may want to use
 the Release configuration for better performance.
-Note: Due to an imcoptibility caused by one of our dependencies, MediaPipe
+Note: Due to an incompatibility caused by one of our dependencies, MediaPipe
 cannot be used for apps running on the iPhone Simulator on Apple Silicon (M1).
 Tip: To switch build configuration in Xcode, click on the target menu, choose
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/
 title: MediaPipe in JavaScript
 parent: Getting Started
 nav_order: 4
@@ -12,6 +13,12 @@ nav_order: 4
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We are moving to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe starting April 3, 2023.*
 ----
 ## Ready-to-use JavaScript Solutions
 MediaPipe currently offers the following solutions:
@@ -29,11 +36,11 @@ Solution                    | NPM Package                             | Example
 Click on a solution link above for more information, including API and code
 snippets.
-### Supported plaforms:
+### Supported platforms:
 | Browser | Platform                | Notes                                  |
 | ------- | ----------------------- | -------------------------------------- |
-| Chrome  | Android / Windows / Mac | Pixel 4 and older unsupported. Fuschia |
+| Chrome  | Android / Windows / Mac | Pixel 4 and older unsupported. Fuchsia |
 |         |                         | unsupported.                           |
 | Chrome  | iOS                     | Camera unavailable in Chrome on iOS.   |
 | Safari  | iPad/iPhone/Mac         | iOS and Safari on iPad / iPhone /      |
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/
 title: MediaPipe in Python
 parent: Getting Started
 has_children: true
@@ -14,6 +15,12 @@ nav_order: 3
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## Ready-to-use Python Solutions
 MediaPipe offers ready-to-use yet customizable Python solutions as a prebuilt
@@ -1,6 +1,6 @@
 ---
-layout: default
+layout: forward
-title: MediaPipe Python Framework
+target: https://developers.google.com/mediapipe/framework/getting_started/python_framework
 parent: MediaPipe in Python
 grand_parent: Getting Started
 nav_order: 1
@@ -12,6 +12,11 @@ nav_order: 1
 1. TOC
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 The MediaPipe Python framework grants direct access to the core components of
 the MediaPipe C++ framework such as Timestamp, Packet, and CalculatorGraph,
@@ -76,7 +81,7 @@ np.ndarray                           | mp::Matrix                    | create_ma
 Google Proto Message                 | Google Proto Message          | create_proto(proto)                                                                                                                                          | get_proto(packet)
 List\[Proto\]                        | std::vector\<Proto\>          | n/a                                                                                                                                                          | get_proto_list(packet)
-It's not uncommon that users create custom C++ classes and and send those into
+It's not uncommon that users create custom C++ classes and send those into
 the graphs and calculators. To allow the custom classes to be used in Python
 with MediaPipe, you may extend the Packet API for a new data type in the
 following steps:
@@ -229,7 +234,7 @@ three stages: initialization and setup, graph run, and graph shutdown.
            output_packets.append(mp.packet_getter.get_str(packet)))
    ```
-    Option 2. Initialize a CalculatorGraph with with a binary protobuf file, and
+    Option 2. Initialize a CalculatorGraph with a binary protobuf file, and
    observe the output stream(s).
    ```python
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/troubleshooting
 title: Troubleshooting
 parent: Getting Started
 nav_order: 10
@@ -12,6 +13,12 @@ nav_order: 10
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 ## Missing Python binary path
 The error message:
@@ -65,7 +72,7 @@ WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/gith
 ```
 usually indicates that Bazel fails to download necessary dependency repositories
-that MediaPipe needs. MedaiPipe has several dependency repositories that are
+that MediaPipe needs. MediaPipe has several dependency repositories that are
 hosted by Google sites. In some regions, you may need to set up a network proxy
 or use a VPN to access those resources. You may also need to append
 `--host_jvm_args "-DsocksProxyHost=<ip address> -DsocksProxyPort=<port number>"`
@@ -6,6 +6,20 @@ nav_order: 1
 ![MediaPipe](https://mediapipe.dev/images/mediapipe_small.png)
 ----
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 *This notice and web page will be removed on June 1, 2023.*
 ----
 <br><br><br><br><br><br><br><br><br><br>
 <br><br><br><br><br><br><br><br><br><br>
 <br><br><br><br><br><br><br><br><br><br>
 --------------------------------------------------------------------------------
 ## Live ML anywhere
@@ -19,6 +33,8 @@ ML solutions for live and streaming media.
 ![ready_to_use.png](https://mediapipe.dev/images/ready_to_use_small.png)                                                             | ![open_source.png](https://mediapipe.dev/images/open_source_small.png)
 ***Ready-to-use solutions***: *Cutting-edge ML solutions demonstrating full power of the framework*            | ***Free and open source***: *Framework and solutions both under Apache 2.0, fully extensible and customizable*
 ----
 ## ML solutions in MediaPipe
 Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris                                                                                                      | Hands                                                                                                      | Pose                                                                                                      | Holistic
@@ -1,3 +1,3 @@
 MediaPipe
 =====================================
-Please see https://docs.mediapipe.dev.
+Please see https://developers.google.com/mediapipe/
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/guide#legacy
 title: AutoFlip (Saliency-aware Video Cropping)
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 14
 ---
@@ -18,6 +19,14 @@ nav_order: 14
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 AutoFlip is an automatic video cropping pipeline built on top of MediaPipe. This
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/guide#legacy
 title: Box Tracking
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 10
 ---
@@ -18,6 +19,14 @@ nav_order: 10
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 MediaPipe Box Tracking has been powering real-time tracking in
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/face_detector/
 title: Face Detection
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 1
 ---
@@ -18,6 +19,14 @@ nav_order: 1
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 MediaPipe Face Detection is an ultrafast face detection solution that comes with
@@ -54,6 +63,25 @@ used for its improved inference speed. Please refer to the
 [model cards](./models.md#face_detection) for details. Default to `0` if not
 specified.
 Note: Not available for JavaScript (use "model" instead).
 #### model
 A string value to indicate which model should be used. Use "short" to
 select a short-range model that works best for faces within 2 meters from the
 camera, and "full" for a full-range model best for faces within 5 meters. For
 the full-range option, a sparse model is used for its improved inference speed.
 Please refer to the model cards for details. Default to empty string.
 Note: Valid only for JavaScript solution.
 #### selfie_mode
 A boolean value to indicate whether to flip the images/video frames
 horizontally or not. Default to `false`.
 Note: Valid only for JavaScript solution.
 #### min_detection_confidence
 Minimum confidence value (`[0.0, 1.0]`) from the face detection model for the
@@ -146,9 +174,9 @@ Please first see general [introduction](../getting_started/javascript.md) on
 MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
 and the following usage example.
-Supported configuration options:
+Supported face detection options:
-
+*   [selfieMode](#selfie_mode)
-*   [modelSelection](#model_selection)
+*   [model](#model)
 *   [minDetectionConfidence](#min_detection_confidence)
 ```html
@@ -176,6 +204,7 @@ Supported configuration options:
 const videoElement = document.getElementsByClassName('input_video')[0];
 const canvasElement = document.getElementsByClassName('output_canvas')[0];
 const canvasCtx = canvasElement.getContext('2d');
 const drawingUtils = window;
 function onResults(results) {
  // Draw the overlays.
@@ -199,7 +228,7 @@ const faceDetection = new FaceDetection({locateFile: (file) => {
  return `https://cdn.jsdelivr.net/npm/@mediapipe/[email protected]/${file}`;
 }});
 faceDetection.setOptions({
-  modelSelection: 0,
+  model: 'short',
  minDetectionConfidence: 0.5
 });
 faceDetection.onResults(onResults);
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/face_landmarker/
 title: Face Mesh
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 2
 ---
@@ -18,6 +19,14 @@ nav_order: 2
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 MediaPipe Face Mesh is a solution that estimates 468 3D face landmarks in
@@ -133,7 +142,7 @@ about the model in this [paper](https://arxiv.org/abs/2006.10962).
 The [Face Landmark Model](#face-landmark-model) performs a single-camera face landmark
 detection in the screen coordinate space: the X- and Y- coordinates are
 normalized screen coordinates, while the Z coordinate is relative and is scaled
-as the X coodinate under the
+as the X coordinate under the
 [weak perspective projection camera model](https://en.wikipedia.org/wiki/3D_projection#Weak_perspective_projection).
 This format is well-suited for some applications, however it does not directly
 enable the full spectrum of augmented reality (AR) features like aligning a
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/image_segmenter/
 title: Hair Segmentation
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 8
 ---
@@ -18,6 +19,14 @@ nav_order: 8
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of April 4, 2023, this solution was upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/image_segmenter/)
 site.*
 ----
 ![hair_segmentation_android_gpu_gif](https://mediapipe.dev/images/mobile/hair_segmentation_android_gpu.gif)
 ## Example Apps
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/hand_landmarker
 title: Hands
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 4
 ---
@@ -18,6 +19,14 @@ nav_order: 4
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution was upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker)
 site.*
 ----
 ## Overview
 The ability to perceive the shape and motion of hands can be a vital component
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://github.com/google/mediapipe/blob/master/docs/solutions/holistic.md
 title: Holistic
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 6
 ---
@@ -18,6 +19,14 @@ nav_order: 6
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 Live perception of simultaneous [human pose](./pose.md),
@@ -66,7 +75,7 @@ previous frame as a guide to the object region on the current one. However,
 during fast movements, the tracker can lose the target, which requires the
 detector to re-localize it in the image. MediaPipe Holistic uses
 [pose](./pose.md) prediction (on every frame) as an additional ROI prior to
-reduce the response time of the pipeline when reacting to fast movements. This
+reducing the response time of the pipeline when reacting to fast movements. This
 also enables the model to retain semantic consistency across the body and its
 parts by preventing a mixup between left and right hands or body parts of one
 person in the frame with another.
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/guide#legacy
 title: Instant Motion Tracking
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 11
 ---
@@ -18,6 +19,14 @@ nav_order: 11
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 Augmented Reality (AR) technology creates fun, engaging, and immersive user
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/face_landmarker/
 title: Iris
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 3
 ---
@@ -18,6 +19,14 @@ nav_order: 3
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 A wide range of real-world applications, including computational photography
@@ -38,7 +47,7 @@ camera, in real-time, without the need for specialized hardware. Through use of
 iris landmarks, the solution is also able to determine the metric distance
 between the subject and the camera with relative error less than 10%. Note that
 iris tracking does not infer the location at which people are looking, nor does
-it provide any form of identity recognition. With the cross-platfrom capability
+it provide any form of identity recognition. With the cross-platform capability
 of the MediaPipe framework, MediaPipe Iris can run on most modern
 [mobile phones](#mobile), [desktops/laptops](#desktop) and even on the
 [web](#web).
@@ -99,7 +108,7 @@ You can also find more details in this
 ### Iris Landmark Model
 The iris model takes an image patch of the eye region and estimates both the eye
-landmarks (along the eyelid) and iris landmarks (along ths iris contour). You
+landmarks (along the eyelid) and iris landmarks (along this iris contour). You
 can find more details in this [paper](https://arxiv.org/abs/2006.11341).
 ![iris_tracking_eye_and_iris_landmarks.png](https://mediapipe.dev/images/mobile/iris_tracking_eye_and_iris_landmarks.png) |
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/guide#legacy
 title: KNIFT (Template-based Feature Matching)
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 13
 ---
@@ -18,6 +19,14 @@ nav_order: 13
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 MediaPipe KNIFT is a template-based feature matching solution using KNIFT
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/guide#legacy
 title: Dataset Preparation with MediaSequence
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 15
 ---
@@ -18,6 +19,14 @@ nav_order: 15
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 MediaPipe is a useful and general framework for media processing that can
@@ -85,7 +94,7 @@ process new data sets, in the documentation of
    MediaSequence uses SequenceExamples as the format of both inputs and
    outputs. Annotations are encoded as inputs in a SequenceExample of metadata
-    that defines the labels and the path to the cooresponding video file. This
+    that defines the labels and the path to the corresponding video file. This
    metadata is passed as input to the C++ `media_sequence_demo` binary, and the
    output is a SequenceExample filled with images and annotations ready for
    model training.
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/guide#legacy
 title: Models and Model Cards
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 30
 ---
@@ -12,6 +13,18 @@ nav_order: 30
 {:toc}
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for
 [these MediaPipe Legacy Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 as of March 1, 2023. All other
 [MediaPipe Legacy Solutions will be upgraded](https://developers.google.com/mediapipe/solutions/guide#legacy)
 to a new MediaPipe Solution. The code repository and prebuilt binaries for all
 MediaPipe Legacy Solutions will continue to be provided on an as-is basis.
 We encourage you to check out the new MediaPipe Solutions at:
 [https://developers.google.com/mediapipe/solutions](https://developers.google.com/mediapipe/solutions)*
 ----
 ### [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)
 *   Short-range model (best for faces within 2 meters from the camera):
@@ -94,6 +107,8 @@ one over the other.
 *   [TFLite model](https://storage.googleapis.com/mediapipe-assets/ssdlite_object_detection.tflite)
 *   [TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/object-detector-quantized_edgetpu.tflite)
 *   [TensorFlow model](https://storage.googleapis.com/mediapipe-assets/object_detection_saved_model/archive.zip)
 *   [Model information](https://storage.googleapis.com/mediapipe-assets/object_detection_saved_model/README.md)
 ### [Objectron](https://google.github.io/mediapipe/solutions/objectron)
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/object_detector/
 title: Object Detection
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 9
 ---
@@ -18,6 +19,14 @@ nav_order: 9
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution was upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/object_detector/)
 site.*
 ----
 ![object_detection_android_gpu.gif](https://mediapipe.dev/images/mobile/object_detection_android_gpu.gif)
 ## Example Apps
@@ -108,9 +117,9 @@ on how to build MediaPipe examples.
 *   With a TensorFlow Model
    This uses the
-    [TensorFlow model](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model)
+    [TensorFlow model](https://storage.googleapis.com/mediapipe-assets/object_detection_saved_model/archive.zip)
    ( see also
-    [model info](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model/README.md)),
+    [model info](https://storage.googleapis.com/mediapipe-assets/object_detection_saved_model/README.md)),
    and the pipeline is implemented in this
    [graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt).
@@ -0,0 +1,89 @@
 ---
 layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/object_detector
 title: Object Detection
 parent: MediaPipe Legacy Solutions
 nav_order: 9
 ---
 # MediaPipe Object Detection
 {: .no_toc }
 <details close markdown="block">
  <summary>
    Table of contents
  </summary>
  {: .text-delta }
 1. TOC
 {:toc}
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution was upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/object_detector)
 site.*
 ----
 ### TensorFlow model
 The model is trained on [MSCOCO 2014](http://cocodataset.org) dataset using [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection). It is a MobileNetV2-based SSD model with 0.5 depth multiplier. Detailed training configuration is in the provided `pipeline.config`. The model is a relatively compact model which has `0.171 mAP` to achieve real-time performance on mobile devices. You can compare it with other models from the [TensorFlow detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md).
 ### TFLite model
 The TFLite model is converted from the TensorFlow above. The steps needed to convert the model are similar to [this tutorial](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193) with minor modifications. Assuming now we have a trained TensorFlow model which includes the checkpoint files and the training configuration file, for example the files provided in this repo:
   * `model.ckpt.index`
   * `model.ckpt.meta`
   * `model.ckpt.data-00000-of-00001`
   * `pipeline.config`
 Make sure you have installed these [python libraries](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1.md). Then to get the frozen graph, run the `export_tflite_ssd_graph.py` script from the `models/research` directory with this command:
 ```bash
 $ PATH_TO_MODEL=path/to/the/model
 $ bazel run object_detection:export_tflite_ssd_graph -- \
    --pipeline_config_path ${PATH_TO_MODEL}/pipeline.config \
    --trained_checkpoint_prefix ${PATH_TO_MODEL}/model.ckpt \
    --output_directory ${PATH_TO_MODEL} \
    --add_postprocessing_op=False
 ```
 The exported model contains two files:
   * `tflite_graph.pb`
   * `tflite_graph.pbtxt`
 The difference between this step and the one in [the tutorial](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193) is that we set `add_postprocessing_op` to False. In MediaPipe, we have provided all the calculators needed for post-processing such that we can exclude the custom TFLite ops for post-processing in the original graph, e.g., non-maximum suppression. This enables the flexibility to integrate with different post-processing algorithms and implementations.
 Optional: You can install and use the [graph tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms) to inspect the input/output of the exported model:
 ```bash
 $ bazel run graph_transforms:summarize_graph -- \
    --in_graph=${PATH_TO_MODEL}/tflite_graph.pb
 ```
 You should be able to see the input image size of the model is 320x320 and the outputs of the model are:
   * `raw_outputs/box_encodings`
   * `raw_outputs/class_predictions`
 The last step is to convert the model to TFLite. You can look at [this guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/r1/convert/cmdline_examples.md) for more detail. For this example, you just need to run:
 ```bash
 $ tflite_convert --  \
  --graph_def_file=${PATH_TO_MODEL}/tflite_graph.pb \
  --output_file=${PATH_TO_MODEL}/model.tflite \
  --input_format=TENSORFLOW_GRAPHDEF \
  --output_format=TFLITE \
  --inference_type=FLOAT \
  --input_shapes=1,320,320,3 \
  --input_arrays=normalized_input_image_tensor \
  --output_arrays=raw_outputs/box_encodings,raw_outputs/class_predictions
 ```
 Now you have the TFLite model `model.tflite` ready to use with MediaPipe Object Detection graphs. Please see the examples for more detail.
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/guide#legacy
 title: Objectron (3D Object Detection)
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 12
 ---
@@ -18,6 +19,14 @@ nav_order: 12
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 ## Overview
 MediaPipe Objectron is a mobile real-time 3D object detection solution for
@@ -170,7 +179,7 @@ and a
 The detection subgraph performs ML inference only once every few frames to
 reduce computation load, and decodes the output tensor to a FrameAnnotation that
 contains nine keypoints: the 3D bounding box's center and its eight vertices.
-The tracking subgraph runs every frame, using the box traker in
+The tracking subgraph runs every frame, using the box tracker in
 [MediaPipe Box Tracking](./box_tracking.md) to track the 2D box tightly
 enclosing the projection of the 3D bounding box, and lifts the tracked 2D
 keypoints to 3D with
@@ -613,7 +622,7 @@ z_ndc = 1 / Z
 ### Pixel Space
-In this API we set upper-left coner of an image as the origin of pixel
+In this API we set upper-left corner of an image as the origin of pixel
 coordinate. One can convert from NDC to pixel space as follows:
 ```
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/
 title: Pose
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 has_children: true
 has_toc: false
 nav_order: 5
@@ -20,6 +21,14 @@ nav_order: 5
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/)
 site.*
 ----
 ## Overview
 Human pose estimation from video plays a critical role in various applications
@@ -134,7 +143,7 @@ The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks
 :----------------------------------------------------------------------------------------------: |
 *Fig 4. 33 pose landmarks.*                                                                      |
-Optionally, MediaPipe Pose can predicts a full-body
+Optionally, MediaPipe Pose can predict a full-body
 [segmentation mask](#segmentation_mask) represented as a two-class segmentation
 (human or background).
@@ -259,6 +268,7 @@ Supported configuration options:
 ```python
 import cv2
 import mediapipe as mp
 import numpy as np
 mp_drawing = mp.solutions.drawing_utils
 mp_drawing_styles = mp.solutions.drawing_styles
 mp_pose = mp.solutions.pose
@@ -1,8 +1,9 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/
 title: Pose Classification
 parent: Pose
-grand_parent: Solutions
+grand_parent: MediaPipe Legacy Solutions
 nav_order: 1
 ---
@@ -19,6 +20,14 @@ nav_order: 1
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/)
 site.*
 ----
 ## Overview
 One of the applications
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/vision/image_segmenter/
 title: Selfie Segmentation
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 7
 ---
@@ -18,6 +19,14 @@ nav_order: 7
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of April 4, 2023, this solution was upgraded to a new MediaPipe
 Solution. For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/image_segmenter/)
 site.*
 ----
 ## Overview
 *Fig 1. Example of MediaPipe Selfie Segmentation.* |
@@ -1,18 +1,40 @@
 ---
 layout: default
-title: Solutions
+title: MediaPipe Legacy Solutions
 nav_order: 3
 has_children: true
 has_toc: false
 ---
-# Solutions
+# MediaPipe Legacy Solutions
 {: .no_toc }
 1. TOC
 {:toc}
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions. We have
 ended support for
 [these MediaPipe Legacy Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 as of March 1, 2023. All other
 [MediaPipe Legacy Solutions will be upgraded](https://developers.google.com/mediapipe/solutions/guide#legacy)
 to a new MediaPipe Solution. The
 [code repository](https://github.com/google/mediapipe/tree/master/mediapipe)
 and prebuilt binaries for all MediaPipe Legacy Solutions will continue to
 be provided on an as-is basis. We encourage you to check out the new MediaPipe
 Solutions at:
 [https://developers.google.com/mediapipe/solutions](https://developers.google.com/mediapipe/solutions)*
 *This notice and web page will be removed on June 1, 2023.*
 ----
 <br><br><br><br><br><br><br><br><br><br>
 <br><br><br><br><br><br><br><br><br><br>
 <br><br><br><br><br><br><br><br><br><br>
 ----
 MediaPipe offers open source cross-platform, customizable ML solutions for live
 and streaming media.
@@ -1,7 +1,8 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/solutions/guide#legacy
 title: YouTube-8M Feature Extraction and Model Inference
-parent: Solutions
+parent: MediaPipe Legacy Solutions
 nav_order: 16
 ---
@@ -18,6 +19,14 @@ nav_order: 16
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 ----
 MediaPipe is a useful and general framework for media processing that can assist
 with research, development, and deployment of ML models. This example focuses on
 model development by demonstrating how to prepare training data and do model
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/
 title: Performance Benchmarking
 parent: Tools
 nav_order: 3
@@ -12,6 +13,12 @@ nav_order: 3
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ---
 *Coming soon.*
 Future mediapipe releases will include tools for visualizing and analysing the
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/
 title: Tools
 nav_order: 4
 has_children: true
@@ -11,3 +12,9 @@ has_children: true
 1. TOC
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/
 title: Tracing and Profiling
 parent: Tools
 nav_order: 2
@@ -12,6 +13,12 @@ nav_order: 2
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ----
 The MediaPipe framework includes a built-in tracer and profiler. The tracer
 records various timing events related to packet processing, including the start
 and end time of each Calculator::Process call. The tracer writes trace log files
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/tools/visualizer
 title: Visualizer
 parent: Tools
 nav_order: 1
@@ -12,6 +13,12 @@ nav_order: 1
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We have moved to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation site for MediaPipe as of April 3, 2023.*
 ---
 To help users understand the structure of their calculator graphs and to
 understand the overall behavior of their machine learning inference pipelines,
 we have built the [MediaPipe Visualizer](https://viz.mediapipe.dev/)
@@ -26,10 +26,11 @@
 namespace mediapipe {
 namespace {
 static bool SafeMultiply(int x, int y, int* result) {
-  static_assert(sizeof(int64) >= 2 * sizeof(int),
+  static_assert(sizeof(int64_t) >= 2 * sizeof(int),
                "Unable to detect overflow after multiplication");
-  const int64 big = static_cast<int64>(x) * static_cast<int64>(y);
+  const int64_t big = static_cast<int64_t>(x) * static_cast<int64_t>(y);
-  if (big > static_cast<int64>(INT_MIN) && big < static_cast<int64>(INT_MAX)) {
+  if (big > static_cast<int64_t>(INT_MIN) &&
      big < static_cast<int64_t>(INT_MAX)) {
    if (result != nullptr) *result = static_cast<int>(big);
    return true;
  } else {
@@ -182,12 +182,12 @@ class SpectrogramCalculator : public CalculatorBase {
  int frame_duration_samples_;
  int frame_overlap_samples_;
  // How many samples we've been passed, used for checking input time stamps.
-  int64 cumulative_input_samples_;
+  int64_t cumulative_input_samples_;
  // How many frames we've emitted, used for calculating output time stamps.
-  int64 cumulative_completed_frames_;
+  int64_t cumulative_completed_frames_;
  // How many frames were emitted last, used for estimating the timestamp on
  // Close when use_local_timestamp_ is true;
-  int64 last_completed_frames_;
+  int64_t last_completed_frames_;
  Timestamp initial_input_timestamp_;
  int num_input_channels_;
  // How many frequency bins we emit (=N_FFT/2 + 1).
@@ -92,8 +92,8 @@ class SpectrogramCalculatorTest
                .cos()
                .transpose();
      }
-      int64 input_timestamp = round(packet_start_time_seconds *
+      int64_t input_timestamp = round(packet_start_time_seconds *
-                                    Timestamp::kTimestampUnitsPerSecond);
+                                      Timestamp::kTimestampUnitsPerSecond);
      AppendInputPacket(packet_data, input_timestamp);
      total_num_input_samples += packet_size_samples;
    }
@@ -116,8 +116,8 @@ class SpectrogramCalculatorTest
      double packet_start_time_seconds =
          kInitialTimestampOffsetMicroseconds * 1e-6 +
          total_num_input_samples / input_sample_rate_;
-      int64 input_timestamp = round(packet_start_time_seconds *
+      int64_t input_timestamp = round(packet_start_time_seconds *
-                                    Timestamp::kTimestampUnitsPerSecond);
+                                      Timestamp::kTimestampUnitsPerSecond);
      std::unique_ptr<Matrix> impulse(
          new Matrix(Matrix::Zero(1, packet_sizes_samples[i])));
      (*impulse)(0, impulse_offsets_samples[i]) = 1.0;
@@ -157,8 +157,8 @@ class SpectrogramCalculatorTest
                .cos()
                .transpose();
      }
-      int64 input_timestamp = round(packet_start_time_seconds *
+      int64_t input_timestamp = round(packet_start_time_seconds *
-                                    Timestamp::kTimestampUnitsPerSecond);
+                                      Timestamp::kTimestampUnitsPerSecond);
      AppendInputPacket(packet_data, input_timestamp);
      total_num_input_samples += packet_size_samples;
    }
@@ -218,7 +218,7 @@ class SpectrogramCalculatorTest
      const double expected_timestamp_seconds =
          packet_timestamp_offset_seconds +
          cumulative_output_frames * frame_step_seconds;
-      const int64 expected_timestamp_ticks =
+      const int64_t expected_timestamp_ticks =
          expected_timestamp_seconds * Timestamp::kTimestampUnitsPerSecond;
      EXPECT_EQ(expected_timestamp_ticks, packet.Timestamp().Value());
      // Accept the timestamp of the first packet as the baseline for checking
@@ -54,7 +54,8 @@ TEST_F(StabilizedLogCalculatorTest, BasicOperation) {
  std::vector<Matrix> input_data_matrices;
  for (int input_packet = 0; input_packet < kNumPackets; ++input_packet) {
-    const int64 timestamp = input_packet * Timestamp::kTimestampUnitsPerSecond;
+    const int64_t timestamp =
        input_packet * Timestamp::kTimestampUnitsPerSecond;
    Matrix input_data_matrix =
        Matrix::Random(kNumChannels, kNumSamples).array().abs();
    input_data_matrices.push_back(input_data_matrix);
@@ -80,7 +81,8 @@ TEST_F(StabilizedLogCalculatorTest, OutputScaleWorks) {
  std::vector<Matrix> input_data_matrices;
  for (int input_packet = 0; input_packet < kNumPackets; ++input_packet) {
-    const int64 timestamp = input_packet * Timestamp::kTimestampUnitsPerSecond;
+    const int64_t timestamp =
        input_packet * Timestamp::kTimestampUnitsPerSecond;
    Matrix input_data_matrix =
        Matrix::Random(kNumChannels, kNumSamples).array().abs();
    input_data_matrices.push_back(input_data_matrix);
@@ -109,7 +109,7 @@ class TimeSeriesFramerCalculator : public CalculatorBase {
  // Returns the timestamp of a sample on a base, which is usually the time
  // stamp of a packet.
  Timestamp CurrentSampleTimestamp(const Timestamp& timestamp_base,
-                                   int64 number_of_samples) {
+                                   int64_t number_of_samples) {
    return timestamp_base + round(number_of_samples / sample_rate_ *
                                  Timestamp::kTimestampUnitsPerSecond);
  }
@@ -118,10 +118,10 @@ class TimeSeriesFramerCalculator : public CalculatorBase {
  // emitted.
  int next_frame_step_samples() const {
    // All numbers are in input samples.
-    const int64 current_output_frame_start = static_cast<int64>(
+    const int64_t current_output_frame_start = static_cast<int64_t>(
        round(cumulative_output_frames_ * average_frame_step_samples_));
    CHECK_EQ(current_output_frame_start, cumulative_completed_samples_);
-    const int64 next_output_frame_start = static_cast<int64>(
+    const int64_t next_output_frame_start = static_cast<int64_t>(
        round((cumulative_output_frames_ + 1) * average_frame_step_samples_));
    return next_output_frame_start - current_output_frame_start;
  }
@@ -134,11 +134,11 @@ class TimeSeriesFramerCalculator : public CalculatorBase {
  // emulate_fractional_frame_overlap is true.
  double average_frame_step_samples_;
  int samples_still_to_drop_;
-  int64 cumulative_output_frames_;
+  int64_t cumulative_output_frames_;
  // "Completed" samples are samples that are no longer needed because
  // the framer has completely stepped past them (taking into account
  // any overlap).
-  int64 cumulative_completed_samples_;
+  int64_t cumulative_completed_samples_;
  Timestamp initial_input_timestamp_;
  // The current timestamp is updated along with the incoming packets.
  Timestamp current_timestamp_;
@@ -49,7 +49,7 @@ class TimeSeriesFramerCalculatorTest
  // Returns a float value with the channel and timestamp separated by
  // an order of magnitude, for easy parsing by humans.
-  float TestValue(int64 timestamp_in_microseconds, int channel) {
+  float TestValue(int64_t timestamp_in_microseconds, int channel) {
    return timestamp_in_microseconds + channel / 10.0;
  }
@@ -59,7 +59,7 @@ class TimeSeriesFramerCalculatorTest
    auto matrix = new Matrix(num_channels, num_samples);
    for (int c = 0; c < num_channels; ++c) {
      for (int i = 0; i < num_samples; ++i) {
-        int64 timestamp = time_series_util::SecondsToSamples(
+        int64_t timestamp = time_series_util::SecondsToSamples(
            starting_timestamp_seconds + i / input_sample_rate_,
            Timestamp::kTimestampUnitsPerSecond);
        (*matrix)(c, i) = TestValue(timestamp, c);
@@ -429,7 +429,7 @@ class TimeSeriesFramerCalculatorTimestampingTest
      num_full_packets -= 1;
    }
-    int64 num_samples = 0;
+    int64_t num_samples = 0;
    for (int packet_num = 0; packet_num < num_full_packets; ++packet_num) {
      const Packet& packet = output().packets[packet_num];
      num_samples += FrameDurationSamples();
@@ -117,6 +117,7 @@ mediapipe_proto_library(
        "//mediapipe/framework:calculator_proto",
        "//mediapipe/framework/formats:classification_proto",
        "//mediapipe/framework/formats:landmark_proto",
        "//mediapipe/framework/formats:time_series_header_proto",
    ],
 )
@@ -197,6 +198,7 @@ cc_library(
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:matrix",
        "//mediapipe/framework/formats:rect_cc_proto",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
@@ -216,9 +218,11 @@ cc_library(
        "//mediapipe/framework:collection_item_id",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:detection_cc_proto",
        "//mediapipe/framework/formats:image",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:matrix",
        "//mediapipe/framework/formats:rect_cc_proto",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
@@ -235,10 +239,12 @@ cc_test(
        ":begin_loop_calculator",
        ":end_loop_calculator",
        ":gate_calculator",
        ":pass_through_calculator",
        "//mediapipe/framework:calculator_context",
        "//mediapipe/framework:calculator_contract",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:packet",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/framework/port:gtest_main",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:parse_text_proto",
@@ -277,6 +283,7 @@ cc_library(
    }),
    deps = [
        ":concatenate_vector_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework/api2:node",
        "//mediapipe/framework/api2:port",
        "//mediapipe/framework/formats:classification_cc_proto",
@@ -285,7 +292,6 @@ cc_library(
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/util:render_data_cc_proto",
        "@org_tensorflow//tensorflow/lite:framework",
    ] + select({
@@ -895,12 +901,12 @@ cc_library(
    }),
    deps = [
        ":split_vector_calculator_cc_proto",
        "//mediapipe/framework/formats:detection_cc_proto",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:rect_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:detection_cc_proto",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:matrix",
        "//mediapipe/framework/formats:rect_cc_proto",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
@@ -1158,6 +1164,7 @@ cc_library(
        "//mediapipe/framework:collection_item_id",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:time_series_header_cc_proto",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
@@ -1329,6 +1336,7 @@ cc_library(
    hdrs = ["merge_to_vector_calculator.h"],
    deps = [
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:packet",
        "//mediapipe/framework/api2:node",
        "//mediapipe/framework/api2:port",
        "//mediapipe/framework/formats:detection_cc_proto",
@@ -20,6 +20,7 @@
 #include "mediapipe/calculators/core/end_loop_calculator.h"
 #include "mediapipe/framework/calculator_contract.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/formats/tensor.h"
 #include "mediapipe/framework/packet.h"
 #include "mediapipe/framework/port/gmock.h"
 #include "mediapipe/framework/port/gtest.h"
@@ -444,5 +445,67 @@ TEST_F(BeginEndLoopCalculatorGraphWithClonedInputsTest, MultipleVectors) {
                  PacketOfIntsEq(input_timestamp2, std::vector<int>{6, 9})));
 }
 absl::Status InitBeginEndTensorLoopTestGraph(
    CalculatorGraph& graph, std::vector<Packet>& output_packets) {
  auto graph_config = ParseTextProtoOrDie<CalculatorGraphConfig>(
      R"pb(
        num_threads: 4
        input_stream: "tensors"
        node {
          calculator: "BeginLoopTensorCalculator"
          input_stream: "ITERABLE:tensors"
          output_stream: "ITEM:tensor"
          output_stream: "BATCH_END:timestamp"
        }
        node {
          calculator: "PassThroughCalculator"
          input_stream: "tensor"
          output_stream: "passed_tensor"
        }
        node {
          calculator: "EndLoopTensorCalculator"
          input_stream: "ITEM:passed_tensor"
          input_stream: "BATCH_END:timestamp"
          output_stream: "ITERABLE:output_tensors"
        }
      )pb");
  tool::AddVectorSink("output_tensors", &graph_config, &output_packets);
  MP_RETURN_IF_ERROR(graph.Initialize(graph_config));
  return graph.StartRun({});
 }
 TEST(BeginEndTensorLoopCalculatorGraphTest, SingleNonEmptyVector) {
  // Initialize the graph.
  CalculatorGraph graph;
  std::vector<Packet> output_packets;
  MP_ASSERT_OK(InitBeginEndTensorLoopTestGraph(graph, output_packets));
  // Prepare the inputs and run.
  Timestamp input_timestamp = Timestamp(0);
  std::vector<mediapipe::Tensor> tensors;
  for (int i = 0; i < 4; i++) {
    tensors.emplace_back(Tensor::ElementType::kFloat32,
                         Tensor::Shape{4, 3, 2, 1});
  }
  Packet vector_packet =
      MakePacket<std::vector<mediapipe::Tensor>>(std::move(tensors));
  MP_ASSERT_OK(graph.AddPacketToInputStream(
      "tensors", std::move(vector_packet).At(input_timestamp)));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  // Verify the output packet.
  EXPECT_EQ(output_packets.size(), 1);
  const std::vector<Tensor>& output_tensors =
      output_packets[0].Get<std::vector<Tensor>>();
  EXPECT_EQ(output_tensors.size(), 4);
  for (int i = 0; i < output_tensors.size(); i++) {
    EXPECT_THAT(output_tensors[i].shape().dims,
                testing::ElementsAre(4, 3, 2, 1));
  }
  MP_ASSERT_OK(graph.CloseAllPacketSources());
  MP_ASSERT_OK(graph.WaitUntilDone());
 }
 }  // namespace
 }  // namespace mediapipe
@@ -20,6 +20,7 @@
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/formats/matrix.h"
 #include "mediapipe/framework/formats/rect.pb.h"
 #include "mediapipe/framework/formats/tensor.h"
 namespace mediapipe {
@@ -55,4 +56,8 @@ REGISTER_CALCULATOR(BeginLoopMatrixVectorCalculator);
 typedef BeginLoopCalculator<std::vector<uint64_t>> BeginLoopUint64tCalculator;
 REGISTER_CALCULATOR(BeginLoopUint64tCalculator);
 // A calculator to process std::vector<mediapipe::Tensor>.
 typedef BeginLoopCalculator<std::vector<Tensor>> BeginLoopTensorCalculator;
 REGISTER_CALCULATOR(BeginLoopTensorCalculator);
 }  // namespace mediapipe
@@ -15,7 +15,6 @@
 #ifndef MEDIAPIPE_CALCULATORS_CORE_BEGIN_LOOP_CALCULATOR_H_
 #define MEDIAPIPE_CALCULATORS_CORE_BEGIN_LOOP_CALCULATOR_H_
 #include "absl/memory/memory.h"
 #include "mediapipe/framework/calculator_context.h"
 #include "mediapipe/framework/calculator_contract.h"
 #include "mediapipe/framework/calculator_framework.h"
@@ -24,6 +23,7 @@
 #include "mediapipe/framework/port/integral_types.h"
 #include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/framework/port/status.h"
 #include "mediapipe/framework/port/status_macros.h"
 namespace mediapipe {
@@ -49,7 +49,7 @@ namespace mediapipe {
 //   calculator:    "EndLoopWithOutputCalculator"
 //   input_stream:  "ITEM:output_of_loop_body"     # ItemU     @loop_internal_ts
 //   input_stream:  "BATCH_END:ext_ts"             # Timestamp @loop_internal_ts
-//   output_stream: "OUTPUT:aggregated_result"     # IterableU @ext_ts
+//   output_stream: "ITERABLE:aggregated_result"   # IterableU @ext_ts
 // }
 //
 // Input streams tagged with "CLONE" are cloned to the corresponding output
@@ -112,13 +112,38 @@ class BeginLoopCalculator : public CalculatorBase {
  absl::Status Process(CalculatorContext* cc) final {
    Timestamp last_timestamp = loop_internal_timestamp_;
    if (!cc->Inputs().Tag("ITERABLE").IsEmpty()) {
-      const IterableT& collection =
+      // Try to consume the ITERABLE packet if possible to obtain the ownership
-          cc->Inputs().Tag("ITERABLE").template Get<IterableT>();
+      // and emit the item packets by moving them.
-      for (const auto& item : collection) {
+      // If the ITERABLE packet is not consumable, then try to copy each item
-        cc->Outputs().Tag("ITEM").AddPacket(
+      // instead. If the ITEM type is not copy constructible, an error will be
-            MakePacket<ItemT>(item).At(loop_internal_timestamp_));
+      // returned.
-        ForwardClonePackets(cc, loop_internal_timestamp_);
+      auto iterable_ptr_or =
-        ++loop_internal_timestamp_;
+          cc->Inputs().Tag("ITERABLE").Value().Consume<IterableT>();
      if (iterable_ptr_or.ok()) {
        for (auto& item : *iterable_ptr_or.value()) {
          Packet item_packet = MakePacket<ItemT>(std::move(item));
          cc->Outputs().Tag("ITEM").AddPacket(
              item_packet.At(loop_internal_timestamp_));
          ForwardClonePackets(cc, loop_internal_timestamp_);
          ++loop_internal_timestamp_;
        }
      } else {
        if constexpr (std::is_copy_constructible<ItemT>()) {
          const IterableT& collection =
              cc->Inputs().Tag("ITERABLE").template Get<IterableT>();
          for (const auto& item : collection) {
            cc->Outputs().Tag("ITEM").AddPacket(
                MakePacket<ItemT>(item).At(loop_internal_timestamp_));
            ForwardClonePackets(cc, loop_internal_timestamp_);
            ++loop_internal_timestamp_;
          }
        } else {
          return absl::InternalError(
              "The element type is not copiable. Consider making the "
              "BeginLoopCalculator the sole owner of the input packet so that "
              "the "
              "items can be consumed and moved.");
        }
      }
    }
@@ -138,7 +163,6 @@ class BeginLoopCalculator : public CalculatorBase {
        .Tag("BATCH_END")
        .AddPacket(MakePacket<Timestamp>(cc->InputTimestamp())
                       .At(Timestamp(loop_internal_timestamp_ - 1)));
    return absl::OkStatus();
  }
@@ -30,7 +30,7 @@ namespace mediapipe {
 typedef ClipVectorSizeCalculator<int> TestClipIntVectorSizeCalculator;
 REGISTER_CALCULATOR(TestClipIntVectorSizeCalculator);
-void AddInputVector(const std::vector<int>& input, int64 timestamp,
+void AddInputVector(const std::vector<int>& input, int64_t timestamp,
                    CalculatorRunner* runner) {
  runner->MutableInputs()->Index(0).packets.push_back(
      MakePacket<std::vector<int>>(input).At(Timestamp(timestamp)));
@@ -63,7 +63,7 @@ void ValidateCombinedLandmarks(
 void AddInputLandmarkLists(
    const std::vector<NormalizedLandmarkList>& input_landmarks_vec,
-    int64 timestamp, CalculatorRunner* runner) {
+    int64_t timestamp, CalculatorRunner* runner) {
  for (int i = 0; i < input_landmarks_vec.size(); ++i) {
    runner->MutableInputs()->Index(i).packets.push_back(
        MakePacket<NormalizedLandmarkList>(input_landmarks_vec[i])
@@ -73,7 +73,7 @@ void AddInputLandmarkLists(
 void AddInputClassificationLists(
    const std::vector<ClassificationList>& input_classifications_vec,
-    int64 timestamp, CalculatorRunner* runner) {
+    int64_t timestamp, CalculatorRunner* runner) {
  for (int i = 0; i < input_classifications_vec.size(); ++i) {
    runner->MutableInputs()->Index(i).packets.push_back(
        MakePacket<ClassificationList>(input_classifications_vec[i])
@@ -46,10 +46,10 @@ MEDIAPIPE_REGISTER_NODE(ConcatenateFloatVectorCalculator);
 //   input_stream: "int32_vector_2"
 //   output_stream: "concatenated_int32_vector"
 // }
-typedef ConcatenateVectorCalculator<int32> ConcatenateInt32VectorCalculator;
+typedef ConcatenateVectorCalculator<int32_t> ConcatenateInt32VectorCalculator;
 MEDIAPIPE_REGISTER_NODE(ConcatenateInt32VectorCalculator);
-typedef ConcatenateVectorCalculator<uint64> ConcatenateUInt64VectorCalculator;
+typedef ConcatenateVectorCalculator<uint64_t> ConcatenateUInt64VectorCalculator;
 MEDIAPIPE_REGISTER_NODE(ConcatenateUInt64VectorCalculator);
 typedef ConcatenateVectorCalculator<bool> ConcatenateBoolVectorCalculator;
@@ -30,26 +30,26 @@ namespace mediapipe {
 typedef ConcatenateVectorCalculator<int> TestConcatenateIntVectorCalculator;
 MEDIAPIPE_REGISTER_NODE(TestConcatenateIntVectorCalculator);
-void AddInputVector(int index, const std::vector<int>& input, int64 timestamp,
+void AddInputVector(int index, const std::vector<int>& input, int64_t timestamp,
                    CalculatorRunner* runner) {
  runner->MutableInputs()->Index(index).packets.push_back(
      MakePacket<std::vector<int>>(input).At(Timestamp(timestamp)));
 }
 void AddInputVectors(const std::vector<std::vector<int>>& inputs,
-                     int64 timestamp, CalculatorRunner* runner) {
+                     int64_t timestamp, CalculatorRunner* runner) {
  for (int i = 0; i < inputs.size(); ++i) {
    AddInputVector(i, inputs[i], timestamp, runner);
  }
 }
-void AddInputItem(int index, int input, int64 timestamp,
+void AddInputItem(int index, int input, int64_t timestamp,
                  CalculatorRunner* runner) {
  runner->MutableInputs()->Index(index).packets.push_back(
      MakePacket<int>(input).At(Timestamp(timestamp)));
 }
-void AddInputItems(const std::vector<int>& inputs, int64 timestamp,
+void AddInputItems(const std::vector<int>& inputs, int64_t timestamp,
                   CalculatorRunner* runner) {
  for (int i = 0; i < inputs.size(); ++i) {
    AddInputItem(i, inputs[i], timestamp, runner);
@@ -279,7 +279,7 @@ TEST(TestConcatenateIntVectorCalculatorTest, MixedVectorsAndItemsAnother) {
 }
 void AddInputVectors(const std::vector<std::vector<float>>& inputs,
-                     int64 timestamp, CalculatorRunner* runner) {
+                     int64_t timestamp, CalculatorRunner* runner) {
  for (int i = 0; i < inputs.size(); ++i) {
    runner->MutableInputs()->Index(i).packets.push_back(
        MakePacket<std::vector<float>>(inputs[i]).At(Timestamp(timestamp)));
@@ -19,6 +19,7 @@
 #include "mediapipe/framework/collection_item_id.h"
 #include "mediapipe/framework/formats/classification.pb.h"
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/formats/time_series_header.pb.h"
 #include "mediapipe/framework/port/canonical_errors.h"
 #include "mediapipe/framework/port/integral_types.h"
 #include "mediapipe/framework/port/ret_check.h"
@@ -84,6 +85,8 @@ class ConstantSidePacketCalculator : public CalculatorBase {
        packet.Set<LandmarkList>();
      } else if (packet_options.has_double_value()) {
        packet.Set<double>();
      } else if (packet_options.has_time_series_header_value()) {
        packet.Set<TimeSeriesHeader>();
      } else {
        return absl::InvalidArgumentError(
            "None of supported values were specified in options.");
@@ -118,6 +121,9 @@ class ConstantSidePacketCalculator : public CalculatorBase {
            MakePacket<LandmarkList>(packet_options.landmark_list_value()));
      } else if (packet_options.has_double_value()) {
        packet.Set(MakePacket<double>(packet_options.double_value()));
      } else if (packet_options.has_time_series_header_value()) {
        packet.Set(MakePacket<TimeSeriesHeader>(
            packet_options.time_series_header_value()));
      } else {
        return absl::InvalidArgumentError(
            "None of supported values were specified in options.");
@@ -19,6 +19,7 @@ package mediapipe;
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/framework/formats/classification.proto";
 import "mediapipe/framework/formats/landmark.proto";
 import "mediapipe/framework/formats/time_series_header.proto";
 message ConstantSidePacketCalculatorOptions {
  extend CalculatorOptions {
@@ -35,6 +36,7 @@ message ConstantSidePacketCalculatorOptions {
      ClassificationList classification_list_value = 6;
      LandmarkList landmark_list_value = 7;
      double double_value = 9;
      TimeSeriesHeader time_series_header_value = 10;
    }
  }
@@ -18,9 +18,11 @@
 #include "mediapipe/framework/formats/classification.pb.h"
 #include "mediapipe/framework/formats/detection.pb.h"
 #include "mediapipe/framework/formats/image.h"
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/formats/matrix.h"
 #include "mediapipe/framework/formats/rect.pb.h"
 #include "mediapipe/framework/formats/tensor.h"
 #include "mediapipe/util/render_data.pb.h"
 #include "tensorflow/lite/interpreter.h"
@@ -52,8 +54,9 @@ typedef EndLoopCalculator<std::vector<::mediapipe::ClassificationList>>
    EndLoopClassificationListCalculator;
 REGISTER_CALCULATOR(EndLoopClassificationListCalculator);
-typedef EndLoopCalculator<std::vector<TfLiteTensor>> EndLoopTensorCalculator;
+typedef EndLoopCalculator<std::vector<TfLiteTensor>>
-REGISTER_CALCULATOR(EndLoopTensorCalculator);
+    EndLoopTfLiteTensorCalculator;
 REGISTER_CALCULATOR(EndLoopTfLiteTensorCalculator);
 typedef EndLoopCalculator<std::vector<::mediapipe::Detection>>
    EndLoopDetectionCalculator;
@@ -62,4 +65,11 @@ REGISTER_CALCULATOR(EndLoopDetectionCalculator);
 typedef EndLoopCalculator<std::vector<Matrix>> EndLoopMatrixCalculator;
 REGISTER_CALCULATOR(EndLoopMatrixCalculator);
 typedef EndLoopCalculator<std::vector<Tensor>> EndLoopTensorCalculator;
 REGISTER_CALCULATOR(EndLoopTensorCalculator);
 typedef EndLoopCalculator<std::vector<::mediapipe::Image>>
    EndLoopImageCalculator;
 REGISTER_CALCULATOR(EndLoopImageCalculator);
 }  // namespace mediapipe
@@ -15,6 +15,8 @@
 #ifndef MEDIAPIPE_CALCULATORS_CORE_END_LOOP_CALCULATOR_H_
 #define MEDIAPIPE_CALCULATORS_CORE_END_LOOP_CALCULATOR_H_
 #include <type_traits>
 #include "mediapipe/framework/calculator_context.h"
 #include "mediapipe/framework/calculator_contract.h"
 #include "mediapipe/framework/calculator_framework.h"
@@ -75,8 +77,23 @@ class EndLoopCalculator : public CalculatorBase {
      if (!input_stream_collection_) {
        input_stream_collection_.reset(new IterableT);
      }
-      input_stream_collection_->push_back(
+      // Try to consume the item and move it into the collection. If the items
-          cc->Inputs().Tag("ITEM").template Get<ItemT>());
+      // are not consumable, then try to copy them instead. If the items are
      // not copiable, then an error will be returned.
      auto item_ptr_or = cc->Inputs().Tag("ITEM").Value().Consume<ItemT>();
      if (item_ptr_or.ok()) {
        input_stream_collection_->push_back(std::move(*item_ptr_or.value()));
      } else {
        if constexpr (std::is_copy_constructible_v<ItemT>) {
          input_stream_collection_->push_back(
              cc->Inputs().Tag("ITEM").template Get<ItemT>());
        } else {
          return absl::InternalError(
              "The item type is not copiable. Consider making the "
              "EndLoopCalculator the sole owner of the input packets so that "
              "it can be moved instead of copying.");
        }
      }
    }
    if (!cc->Inputs().Tag("BATCH_END").Value().IsEmpty()) {  // flush signal
@@ -35,7 +35,6 @@ message FlowLimiterCalculatorOptions {
  optional int32 max_in_queue = 2 [default = 0];
  // The maximum time in microseconds to wait for a frame to finish processing.
-  // The default value stops waiting after 1 sec.
+  // The default value 0 specifies no timeout.
-  // The value 0 specifies no timeout.
+  optional int64 in_flight_timeout = 3 [default = 0];
  optional int64 in_flight_timeout = 3 [default = 1000000];
 }
@@ -61,8 +61,8 @@ class AtomicSemaphore {
 };
 // Returns the timestamp values for a vector of Packets.
-std::vector<int64> TimestampValues(const std::vector<Packet>& packets) {
+std::vector<int64_t> TimestampValues(const std::vector<Packet>& packets) {
-  std::vector<int64> result;
+  std::vector<int64_t> result;
  for (const Packet& packet : packets) {
    result.push_back(packet.Timestamp().Value());
  }
@@ -180,9 +180,9 @@ TEST_F(FlowLimiterCalculatorSemaphoreTest, FramesDropped) {
  InitializeGraph(1);
  MP_ASSERT_OK(graph_.StartRun({}));
-  auto send_packet = [this](const std::string& input_name, int64 n) {
+  auto send_packet = [this](const std::string& input_name, int64_t n) {
    MP_EXPECT_OK(graph_.AddPacketToInputStream(
-        input_name, MakePacket<int64>(n).At(Timestamp(n))));
+        input_name, MakePacket<int64_t>(n).At(Timestamp(n))));
  };
  Packet allow_packet;
@@ -207,12 +207,12 @@ TEST_F(FlowLimiterCalculatorSemaphoreTest, FramesDropped) {
  EXPECT_EQ(10, out_1_packets_.size());
  // Timestamps have not been altered.
-  EXPECT_EQ(PacketValues<int64>(out_1_packets_),
+  EXPECT_EQ(PacketValues<int64_t>(out_1_packets_),
            TimestampValues(out_1_packets_));
  // Extra inputs on in_1 have been dropped.
  EXPECT_EQ(TimestampValues(out_1_packets_),
-            (std::vector<int64>{0, 10, 20, 30, 40, 50, 60, 70, 80, 90}));
+            (std::vector<int64_t>{0, 10, 20, 30, 40, 50, 60, 70, 80, 90}));
 }
 // A calculator that sleeps during Process.
@@ -221,8 +221,8 @@ class SleepCalculator : public CalculatorBase {
  static absl::Status GetContract(CalculatorContract* cc) {
    cc->Inputs().Tag(kPacketTag).SetAny();
    cc->Outputs().Tag(kPacketTag).SetSameAs(&cc->Inputs().Tag(kPacketTag));
-    cc->InputSidePackets().Tag(kSleepTimeTag).Set<int64>();
+    cc->InputSidePackets().Tag(kSleepTimeTag).Set<int64_t>();
-    cc->InputSidePackets().Tag(kWarmupTimeTag).Set<int64>();
+    cc->InputSidePackets().Tag(kWarmupTimeTag).Set<int64_t>();
    cc->InputSidePackets().Tag(kClockTag).Set<mediapipe::Clock*>();
    cc->SetTimestampOffset(0);
    return absl::OkStatus();
@@ -237,8 +237,8 @@ class SleepCalculator : public CalculatorBase {
    ++packet_count;
    absl::Duration sleep_time = absl::Microseconds(
        packet_count == 1
-            ? cc->InputSidePackets().Tag(kWarmupTimeTag).Get<int64>()
+            ? cc->InputSidePackets().Tag(kWarmupTimeTag).Get<int64_t>()
-            : cc->InputSidePackets().Tag(kSleepTimeTag).Get<int64>());
+            : cc->InputSidePackets().Tag(kSleepTimeTag).Get<int64_t>());
    clock_->Sleep(sleep_time);
    cc->Outputs()
        .Tag(kPacketTag)
@@ -375,8 +375,8 @@ TEST_F(FlowLimiterCalculatorTest, FinishedTimestamps) {
  std::map<std::string, Packet> side_packets = {
      {"limiter_options",
       MakePacket<FlowLimiterCalculatorOptions>(limiter_options)},
-      {"warmup_time", MakePacket<int64>(22000)},
+      {"warmup_time", MakePacket<int64_t>(22000)},
-      {"sleep_time", MakePacket<int64>(22000)},
+      {"sleep_time", MakePacket<int64_t>(22000)},
      {"drop_timesamps", MakePacket<bool>(false)},
      {"clock", MakePacket<mediapipe::Clock*>(clock_)},
  };
@@ -447,8 +447,8 @@ TEST_F(FlowLimiterCalculatorTest, FinishedLost) {
  std::map<std::string, Packet> side_packets = {
      {"limiter_options",
       MakePacket<FlowLimiterCalculatorOptions>(limiter_options)},
-      {"warmup_time", MakePacket<int64>(22000)},
+      {"warmup_time", MakePacket<int64_t>(22000)},
-      {"sleep_time", MakePacket<int64>(22000)},
+      {"sleep_time", MakePacket<int64_t>(22000)},
      {"drop_timesamps", MakePacket<bool>(true)},
      {"clock", MakePacket<mediapipe::Clock*>(clock_)},
  };
@@ -511,8 +511,8 @@ TEST_F(FlowLimiterCalculatorTest, FinishedDelayed) {
  std::map<std::string, Packet> side_packets = {
      {"limiter_options",
       MakePacket<FlowLimiterCalculatorOptions>(limiter_options)},
-      {"warmup_time", MakePacket<int64>(500000)},
+      {"warmup_time", MakePacket<int64_t>(500000)},
-      {"sleep_time", MakePacket<int64>(22000)},
+      {"sleep_time", MakePacket<int64_t>(22000)},
      {"drop_timesamps", MakePacket<bool>(false)},
      {"clock", MakePacket<mediapipe::Clock*>(clock_)},
  };
@@ -606,8 +606,8 @@ TEST_F(FlowLimiterCalculatorTest, TwoInputStreams) {
  std::map<std::string, Packet> side_packets = {
      {"limiter_options",
       MakePacket<FlowLimiterCalculatorOptions>(limiter_options)},
-      {"warmup_time", MakePacket<int64>(22000)},
+      {"warmup_time", MakePacket<int64_t>(22000)},
-      {"sleep_time", MakePacket<int64>(22000)},
+      {"sleep_time", MakePacket<int64_t>(22000)},
      {"drop_timesamps", MakePacket<bool>(true)},
      {"clock", MakePacket<mediapipe::Clock*>(clock_)},
  };
@@ -715,8 +715,8 @@ TEST_F(FlowLimiterCalculatorTest, ZeroQueue) {
  std::map<std::string, Packet> side_packets = {
      {"limiter_options",
       MakePacket<FlowLimiterCalculatorOptions>(limiter_options)},
-      {"warmup_time", MakePacket<int64>(12000)},
+      {"warmup_time", MakePacket<int64_t>(12000)},
-      {"sleep_time", MakePacket<int64>(12000)},
+      {"sleep_time", MakePacket<int64_t>(12000)},
      {"drop_timesamps", MakePacket<bool>(true)},
      {"clock", MakePacket<mediapipe::Clock*>(clock_)},
  };
@@ -862,9 +862,9 @@ TEST_F(FlowLimiterCalculatorTest, AuxiliaryInputs) {
  std::map<std::string, Packet> side_packets = {
      // Fake processing lazy initialization time in microseconds.
-      {"warmup_time", MakePacket<int64>(22000)},
+      {"warmup_time", MakePacket<int64_t>(22000)},
      // Fake processing duration in microseconds.
-      {"sleep_time", MakePacket<int64>(22000)},
+      {"sleep_time", MakePacket<int64_t>(22000)},
      // The SimulationClock to count virtual elapsed time.
      {"clock", MakePacket<mediapipe::Clock*>(clock_)},
  };
@@ -125,7 +125,6 @@ class GateCalculator : public CalculatorBase {
    RET_CHECK_OK(CheckAndInitAllowDisallowInputs(cc));
    const int num_data_streams = cc->Inputs().NumEntries("");
    RET_CHECK_GE(num_data_streams, 1);
    RET_CHECK_EQ(cc->Outputs().NumEntries(""), num_data_streams)
        << "Number of data output streams must match with data input streams.";
@@ -52,6 +52,15 @@ class GateCalculatorTest : public ::testing::Test {
    MP_ASSERT_OK(runner_->Run()) << "Calculator execution failed.";
  }
  void RunTimeStepWithoutDataStream(int64_t timestamp,
                                    const std::string& control_tag,
                                    bool control) {
    runner_->MutableInputs()
        ->Tag(control_tag)
        .packets.push_back(MakePacket<bool>(control).At(Timestamp(timestamp)));
    MP_ASSERT_OK(runner_->Run()) << "Calculator execution failed.";
  }
  void SetRunner(const std::string& proto) {
    runner_ = absl::make_unique<CalculatorRunner>(
        ParseTextProtoOrDie<CalculatorGraphConfig::Node>(proto));
@@ -332,6 +341,35 @@ TEST_F(GateCalculatorTest, AllowWithStateChange) {
  EXPECT_EQ(false, output[1].Get<bool>());  // Disallow.
 }
 TEST_F(GateCalculatorTest, AllowWithStateChangeNoDataStreams) {
  SetRunner(R"(
        calculator: "GateCalculator"
        input_stream: "ALLOW:gating_stream"
        output_stream: "STATE_CHANGE:state_changed"
  )");
  constexpr int64_t kTimestampValue0 = 42;
  RunTimeStepWithoutDataStream(kTimestampValue0, "ALLOW", false);
  constexpr int64_t kTimestampValue1 = 43;
  RunTimeStepWithoutDataStream(kTimestampValue1, "ALLOW", true);
  constexpr int64_t kTimestampValue2 = 44;
  RunTimeStepWithoutDataStream(kTimestampValue2, "ALLOW", true);
  constexpr int64_t kTimestampValue3 = 45;
  RunTimeStepWithoutDataStream(kTimestampValue3, "ALLOW", false);
  LOG(INFO) << "a";
  const std::vector<Packet>& output =
      runner()->Outputs().Get("STATE_CHANGE", 0).packets;
  LOG(INFO) << "s";
  ASSERT_EQ(2, output.size());
  LOG(INFO) << "d";
  EXPECT_EQ(kTimestampValue1, output[0].Timestamp().Value());
  EXPECT_EQ(kTimestampValue3, output[1].Timestamp().Value());
  LOG(INFO) << "f";
  EXPECT_EQ(true, output[0].Get<bool>());   // Allow.
  EXPECT_EQ(false, output[1].Get<bool>());  // Disallow.
  LOG(INFO) << "g";
 }
 TEST_F(GateCalculatorTest, DisallowWithStateChange) {
  SetRunner(R"(
        calculator: "GateCalculator"
@@ -359,6 +397,31 @@ TEST_F(GateCalculatorTest, DisallowWithStateChange) {
  EXPECT_EQ(false, output[1].Get<bool>());  // Disallow.
 }
 TEST_F(GateCalculatorTest, DisallowWithStateChangeNoDataStreams) {
  SetRunner(R"(
        calculator: "GateCalculator"
        input_stream: "DISALLOW:gating_stream"
        output_stream: "STATE_CHANGE:state_changed"
  )");
  constexpr int64_t kTimestampValue0 = 42;
  RunTimeStepWithoutDataStream(kTimestampValue0, "DISALLOW", true);
  constexpr int64_t kTimestampValue1 = 43;
  RunTimeStepWithoutDataStream(kTimestampValue1, "DISALLOW", false);
  constexpr int64_t kTimestampValue2 = 44;
  RunTimeStepWithoutDataStream(kTimestampValue2, "DISALLOW", false);
  constexpr int64_t kTimestampValue3 = 45;
  RunTimeStepWithoutDataStream(kTimestampValue3, "DISALLOW", true);
  const std::vector<Packet>& output =
      runner()->Outputs().Get("STATE_CHANGE", 0).packets;
  ASSERT_EQ(2, output.size());
  EXPECT_EQ(kTimestampValue1, output[0].Timestamp().Value());
  EXPECT_EQ(kTimestampValue3, output[1].Timestamp().Value());
  EXPECT_EQ(true, output[0].Get<bool>());   // Allow.
  EXPECT_EQ(false, output[1].Get<bool>());  // Disallow.
 }
 // Must not detect disallow value for first timestamp as a state change.
 TEST_F(GateCalculatorTest, DisallowInitialNoStateTransition) {
  SetRunner(R"(
@@ -94,17 +94,17 @@ class GraphProfileCalculatorTest : public ::testing::Test {
                                                      &graph_config_));
  }
-  static Packet PacketAt(int64 ts) {
+  static Packet PacketAt(int64_t ts) {
-    return Adopt(new int64(999)).At(Timestamp(ts));
+    return Adopt(new int64_t(999)).At(Timestamp(ts));
  }
  static Packet None() { return Packet().At(Timestamp::OneOverPostStream()); }
  static bool IsNone(const Packet& packet) {
    return packet.Timestamp() == Timestamp::OneOverPostStream();
  }
  // Return the values of the timestamps of a vector of Packets.
-  static std::vector<int64> TimestampValues(
+  static std::vector<int64_t> TimestampValues(
      const std::vector<Packet>& packets) {
-    std::vector<int64> result;
+    std::vector<int64_t> result;
    for (const Packet& p : packets) {
      result.push_back(p.Timestamp().Value());
    }
@@ -191,17 +191,17 @@ class ImmediateMuxCalculatorTest : public ::testing::Test {
                                                      &graph_config_));
  }
-  static Packet PacketAt(int64 ts) {
+  static Packet PacketAt(int64_t ts) {
-    return Adopt(new int64(999)).At(Timestamp(ts));
+    return Adopt(new int64_t(999)).At(Timestamp(ts));
  }
  static Packet None() { return Packet().At(Timestamp::OneOverPostStream()); }
  static bool IsNone(const Packet& packet) {
    return packet.Timestamp() == Timestamp::OneOverPostStream();
  }
  // Return the values of the timestamps of a vector of Packets.
-  static std::vector<int64> TimestampValues(
+  static std::vector<int64_t> TimestampValues(
      const std::vector<Packet>& packets) {
-    std::vector<int64> result;
+    std::vector<int64_t> result;
    for (const Packet& p : packets) {
      result.push_back(p.Timestamp().Value());
    }
@@ -48,7 +48,6 @@ class MergeToVectorCalculator : public Node {
  }
  absl::Status Process(CalculatorContext* cc) {
    const int input_num = kIn(cc).Count();
    std::vector<T> output_vector;
    for (auto it = kIn(cc).begin(); it != kIn(cc).end(); it++) {
      const auto& elem = *it;
--- a/Show More
+++ b/Show More