namespace mlir {
namespace TFL {
namespace tac {

// A simple hardware is an interface makes you add a target backend easily if
// you don't want too much customization.
//
// It allows you to easily specify the ops capabilities (by
// specifying the denylist), the rest ops will be considered supported. Also you
// can also specify the advantage over CPU.
//

	var rootfsPath string

	cmds := &cobra.Command{
		Use:   "kubeadm",
		Short: "kubeadm: easily bootstrap a secure Kubernetes cluster",
		Long: dedent.Dedent(`

			    ┌──────────────────────────────────────────────────────────┐
			    │ KUBEADM                                                  │
			    │ Easily bootstrap a secure Kubernetes cluster             │
			    │                                                          │

version: 2
updates:
# TODO(b/170636568): Enable Maven updates? Perhaps wait until we can more
# easily import the generated PRs into our internal repo.
#  - package-ecosystem: "maven"
#    directory: "/"
#    schedule:
#      interval: "weekly"
#    groups:
#      dependencies:
#        applies-to: version-updates
#        patterns:
#          - "*"
#  - package-ecosystem: "maven"
#    directory: "/android"
#    schedule:

  EXPECT_EQ(tf2xla_fallback_count, 322);
  EXPECT_EQ(non_categorized_count, 428);
}

// Just a counter test to see which ops have duplicate lowerings. This isn't a
// correctness test versus a test to easily ensure the op registry is kept
// in sync.
TEST_F(LegalizationOpConfigTest, CountTypesWhichHaveBothMlirAndTf2xlaFallback) {
  int double_lowering_count = 0;

  DialectRegistry dialect_registry;

# Test case for #67623 in go.mod files: make sure the error for
# an unknown godebug is printed on a line starting with the file
# and line number, so it can be easily parsed by tools.

! go list
stderr '^go.mod:3: unknown godebug "foo"$'

-- go.mod --
module example.com/bar

Architecture: all
Maintainer: Fess Team
Depends: libc6, adduser
Section: web
Priority: optional
Homepage: https://github.com/codelibs/fess
Description: Enterprise Search Server: Fess

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

module example.com
go 1.12

-- a.go --
package a

/*
typedef int T;
*/
import "C"

func (C.T) f() {}
func (recv *C.T) g() {}

// The check is more education than enforcement,
// and is easily defeated using a type alias.
type Alias = C.T
func (Alias) h() {}

## How the configuration works

We use Kotlin portable DSL to store TeamCity configuration, which means you can easily create a new pipeline
based on a specific branch. Currently, we have two pipelines: `master` and `release`, but you can easily create
and test another isolated pipeline from any branch. 

We'll explain everything via an example. Let's say you make some changes on your branch `myTestBranch`

converter, CPU kernels, etc.). Writing shape functions and gradients for these
ops is extremely difficult. `tf.function` makes some parts of the implementation
simpler, but it introduces runtime overhead and it cannot easily be used to
apply dedicated optimizations to op kernels.

The composable TF framework allows the user to define portable TF ops as
compositions of other TF ops. It translates a Python function used to define the