Field {
        override fun toString() = "field"
    },
    PropertyUsage {
        override fun toString() = "property usage"
    },
    InputProperty {
        override fun toString() = "input property"
    },
    OutputProperty {
        override fun toString() = "output property"
    }

	// (We'll add another blank line after our comment block.)
	// When we start dropping // +build comments, we can skip over /* */ comments too.
	// Note that we are processing tabwriter input, so every comment
	// begins and ends with a tabwriter.Escape byte.
	// And some newlines have turned into \f bytes.
	insert := 0
	for pos := 0; ; {
		// Skip leading space at beginning of line.
		blank := true

  variable_names.reserve(resource_names.size());
  for (StringRef name : resource_names) variable_names.push_back(name.str());

  std::vector<Tensor> resource_tensors;
  Status status = session->Run(
      /*inputs=*/{}, variable_names,
      /*target_tensor_names=*/{}, &resource_tensors);
  if (!status.ok()) {
    return module.emitOpError()
           << "failed to run the provided session: " << status.message();
  }

var hflag = flag.Bool("h", false, "Panic on fatal errors (for stack trace)")
var hwflag = flag.Bool("hw", false, "Panic on warnings (for stack trace)")
var indirsflag = flag.String("i", "", "Input dirs to examine (comma separated)")
var pkgpatflag = flag.String("pkg", "", "Restrict output to package(s) matching specified package pattern.")
var cpuprofileflag = flag.String("cpuprofile", "", "Write CPU profile to specified file")

NOTE: You may encounter a few cached tasks even in the first of the two builds, where no previously cached results should be available.
This can happen if you have tasks in your build that are configured to produce the same results from the same inputs; in such a case once one of these tasks has finished, Gradle will simply reuse its output for the rest of the tasks.

  auto copied_model = std::make_unique<tflite::ModelT>();
  input_model->UnPackTo(copied_model.get(), nullptr);
  return copied_model;
}
}  // namespace

// TODO(b/214314076): Support MLIR model as an input for the C++ dynamic range
// quantization API
TfLiteStatus QuantizeWeights(
    flatbuffers::FlatBufferBuilder* builder, const tflite::Model* input_model,
    const tflite::TensorType& inference_type,

	// Prints `mc` cli configuration message chooses
	// first endpoint as default.
	printCLIAccessMsg(strippedAPIEndpoints[0], "myminio")

	// Prints documentation message.
	printObjectAPIMsg()
}

// Returns true if input is IPv6
func isIPv6(host string) bool {
	h, _, err := net.SplitHostPort(host)
	if err != nil {
		h = host
	}
	ip := net.ParseIP(h)
	return ip.To16() != nil && ip.To4() == nil
}

// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package cmd

import (
	"errors"
)

// errInvalidArgument means that input argument is invalid.
var errInvalidArgument = errors.New("Invalid arguments specified")

// errMethodNotAllowed means that method is not allowed.
var errMethodNotAllowed = errors.New("Method not allowed")

			args = append(args, msg)
			msg = "%s"
		}
		console.pretty(msg+"\n", args...)
	}
}

// Fatal prints only fatal error message with no stack trace
// it will be called for input validation failures
func Fatal(err error, msg string, data ...interface{}) {
	fatal(err, msg, data...)
}

func fatal(err error, msg string, data ...interface{}) {
	if msg == "" {
		if len(data) > 0 {

// RUN: tf-tfrt-opt -split-input-file -verify-diagnostics -lower-to-ifrt-restore-variable %s | FileCheck %s


// -----
// single variable

// CHECK-LABEL:   func.func @restore_single() {
// CHECK-NEXT:     [[PREFIX:%.*]] = "tf.Const"() <{value = dense<"restore_ariables"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
// CHECK-NEXT:     [[SLICE:%.*]] = "tf.Const"() <{value = dense<""> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>