darwin=false;
mingw=false
case "`uname`" in
  CYGWIN*) cygwin=true ;;
  MINGW*) mingw=true;;
  Darwin*) darwin=true
    # Use /usr/libexec/java_home if available, otherwise fall back to /Library/Java/Home
    # See https://developer.apple.com/library/mac/qa/qa1170/_index.html
    if [ -z "$JAVA_HOME" ]; then
      if [ -x "/usr/libexec/java_home" ]; then
        export JAVA_HOME="`/usr/libexec/java_home`"
      else

		appID, err := plistExtract(fname, "Entitlements:application-identifier")
		check(err)
		fmt.Printf("export GOIOS_APP_ID=%s\n", appID)

		teamID, err := plistExtract(fname, "Entitlements:com.apple.developer.team-identifier")
		check(err)
		fmt.Printf("export GOIOS_TEAM_ID=%s\n", teamID)
	}
}

func detectMobileProvisionFiles(udids [][]byte) []string {
	cmd := exec.Command("mdfind", "-name", ".mobileprovision")

<dict>
	<key>keychain-access-groups</key>
	<array><string>` + appID + `</string></array>
	<key>get-task-allow</key>
	<true/>
	<key>application-identifier</key>
	<string>` + appID + `</string>
	<key>com.apple.developer.team-identifier</key>
	<string>` + teamID + `</string>
</dict>
</plist>
`
}

const resourceRules = `<?xml version="1.0" encoding="UTF-8"?>

build:cuda_clang --@local_config_cuda//:cuda_compiler=clang
build:cuda_clang --copt=-Qunused-arguments
# Select supported compute capabilities (supported graphics cards).
# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX
# is forward compatible beyond the current compute capability major
# release while SASS is only forward compatible inside the current

    <root-XML localName="document" namespaceURI="http://developer.apple.com/namespaces/sl" />
    <sub-class-of type="application/vnd.apple.iwork" />
    <glob pattern="*.pages"/>
  </mime-type>
  <mime-type type="application/vnd.apple.numbers">
    <root-XML localName="document" namespaceURI="http://developer.apple.com/namespaces/ls" />
    <sub-class-of type="application/vnd.apple.iwork" />

        `tf.distribute.experimental.MultiWorkerMirroredStrategy`
    *   Update NVIDIA `NCCL` to `2.5.7-1` for better performance and performance
        tuning. Please see
        [nccl developer guide](https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/docs/env.html)
        for more information on this.
    *   Support gradient `allreduce` in `float16`. See this

    ask_cuda_compute_capabilities = (
        'Please specify a list of comma-separated CUDA compute capabilities '
        'you want to build with.\nYou can find the compute capability of your '
        'device at: https://developer.nvidia.com/cuda-gpus. Each capability '
        'can be specified as "x.y" or "compute_xy" to include both virtual and'
        ' binary GPU code, or as "sm_xy" to only include the binary '

[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine

    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }
    ],
    "access_right": "open",

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).