{GOOS: "freebsd", GOARCH: "riscv64"},
	{GOOS: "netbsd", GOARCH: "386", CgoEnabled: true},
	{GOOS: "netbsd", GOARCH: "386"},
	{GOOS: "netbsd", GOARCH: "amd64", CgoEnabled: true},
	{GOOS: "netbsd", GOARCH: "amd64"},
	{GOOS: "netbsd", GOARCH: "arm", CgoEnabled: true},
	{GOOS: "netbsd", GOARCH: "arm"},
	{GOOS: "netbsd", GOARCH: "arm64", CgoEnabled: true},
	{GOOS: "netbsd", GOARCH: "arm64"},

 all.bash on the iOS emulator:

	GOOS=ios GOARCH=amd64 CGO_ENABLED=1 CC_FOR_TARGET=$(pwd)/../misc/ios/clangwrap.sh ./all.bash

If CC_FOR_TARGET is not set when the toolchain is built (make.bash or all.bash), CC
can be set on the command line. For example,

	GOOS=ios GOARCH=amd64 CGO_ENABLED=1 CC=$(go env GOROOT)/misc/ios/clangwrap.sh go build

	"log"
	"os"

	"cmd/asm/internal/arch"
	"cmd/asm/internal/asm"
	"cmd/asm/internal/flags"
	"cmd/asm/internal/lex"

	"cmd/internal/bio"
	"cmd/internal/obj"
	"cmd/internal/objabi"
	"cmd/internal/telemetry/counter"
)

func main() {
	log.SetFlags(0)
	log.SetPrefix("asm: ")
	counter.Open()

	buildcfg.Check()
	GOARCH := buildcfg.GOARCH

	flags.Parse()
	counter.Inc("asm/invocations")

failed=false
for target in $targets
do
	echo ""
	echo "### Building $target"
	export GOOS=$(echo $target | sed 's/-.*//')
	export GOARCH=$(echo $target | sed 's/.*-//')
	unset GOARM
	if [ "$GOARCH" = "arm5" ]; then
		export GOARCH=arm
		export GOARM=5
	fi

	# Build and vet everything.
	# cmd/go/internal/work/exec.go enables the same vet flags during go test of std cmd

mobile subrepository:

	https://github.com/golang/mobile

To run the standard library tests, enable Cgo and use an appropriate
C compiler from the Android NDK. For example,

	CGO_ENABLED=1 \
	GOOS=android \
	GOARCH=arm64 \
	CC_FOR_TARGET=$NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android21-clang \
	./all.bash

To run tests on the Android device, add the bin directory to PATH so the

#### 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.

    - `apiserver_flowcontrol_nominal_limit_seats`: Nominal number of execution seats configured for each priority level
    - `apiserver_flowcontrol_lower_limit_seats`: Configured lower bound on number of execution seats available to each priority level
    - `apiserver_flowcontrol_upper_limit_seats`: Configured upper bound on number of execution seats available to each priority level

        while (restCapacity > 0 && list.isNotEmpty() && buckets.size < maxNumberInBucket) {
            val smallestElement = list.findLast { searched -> buckets.all { canRunTogether(it, searched) } } ?: break
            list.remove(smallestElement)
            buckets.add(smallestElement)
            restCapacity -= toIntFunction(smallestElement)
        }

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems

## Ports {#ports}

### Windows

<!-- go.dev/issue/71671 -->