using tensorflow::AttrValueMap;
using tensorflow::DataType;
using tensorflow::ExtendSessionGraphHelper;
using tensorflow::FullTypeDef;
using tensorflow::Graph;
using tensorflow::GraphDef;
using tensorflow::mutex_lock;
using tensorflow::NameRangeMap;
using tensorflow::NameRangesForNode;
using tensorflow::NewSession;
using tensorflow::Node;
using tensorflow::NodeBuilder;
using tensorflow::OpDef;
using tensorflow::PartialTensorShape;

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

# Release 2.11.1

**Note**: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.

#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

name: ARM CI Extended

on:
  push:
    tags:
      - v2.**
  schedule:
    - cron: '0 4 * * *'

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      fail-fast: false
      matrix:
        pyver: ['3.9', '3.10', '3.11', '3.12']
    steps:

on:
  push:
    tags:
      - v2.**
    branches:
      - r2.**
  schedule:
    - cron: '0 8 * * *'

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      fail-fast: false
      matrix:
        pyver: ['3.9', '3.10', '3.11', '3.12']
    steps:

---
name: TensorFlow Lite in Play Services issue
about: Use this template for issues with TensorFlow Lite in Google Play Services
labels: 'comp:lite-in-play-services'

---

**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
  if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version

# Official CI Directory

Maintainer: TensorFlow and TensorFlow DevInfra

Issue Reporting: File an issue against this repo and tag
[@devinfra](https://github.com/orgs/tensorflow/teams/devinfra)

********************************************************************************

## TensorFlow's Official CI and Build/Test Scripts

TensorFlow's official CI jobs run the scripts in this folder. Our internal CI

{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

#define TENSORFLOW_C_C_API_EXPERIMENTAL_H_

#include <stddef.h>
#include <stdint.h>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_macros.h"
#include "tensorflow/c/eager/c_api.h"

// --------------------------------------------------------------------------
// Experimental C API for TensorFlow.
//
// The API here is subject to changes in the future.