}
	}

	var inputs []string

	// Parse parameters
	switch {
	case *stdin:
		// Parse 'mc support inspect --json' output
		input := struct {
			File string `json:"file"`
			Key  string `json:"key"`
		}{}
		got, err := io.ReadAll(os.Stdin)
		if err != nil {
			fatalErr(err)
		}
		fatalErr(json.Unmarshal(got, &input))
		inputs = []string{input.File}
		*keyHex = input.Key

                && Objects.equals(inputs, that.inputs);
    }

    @Override
    public int hashCode() {
        return Objects.hash(modelId, location, inputs);
    }

    Stream<InputSource> sources() {
        return inputs != null ? inputs.stream() : Stream.of(this);
    }

    @Override
    public String toString() {
        if (inputs != null) {

jobs:
  cherrypick:
    name: Cherrypick to ${{ github.event.inputs.release_branch}} - ${{ github.event.inputs.git_commit }}
    runs-on: ubuntu-latest
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    steps:
    - name: Checkout code
      uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
      with:
        ref: ${{ github.event.inputs.release_branch }}

                    inputs = new String[inputStrings.size()];
                    for (int i = 0; i < inputs.length; i++) {
                        inputs[i] = unescape(inputStrings.get(i)).trim();
                    }

                    if (inputs.length > 0) {
                        id++;
                        final SynonymItem item = new SynonymItem(id, inputs, inputs);

  parallel_inputs.reserve(inputs.size());
  implicitly_broadcast_tensors.reserve(inputs.size());  // not tight
  for (const auto& input : inputs) {
    if (absl::holds_alternative<TFE_TensorHandle*>(input)) {
      if (operation_name == std::string("_EagerConst")) {
        // Non-parallel tensors from _EagerConst/tf.constant are implicitly
        // broadcast, i.e. set as the input to each parallel operation. This

    std::vector<TFE_TensorHandle*> device_inputs;
    device_inputs.reserve(inputs.size());
    for (int input_index = 0; input_index < inputs.size(); ++input_index) {
      // Parallel tensors are divided between operations by device.
      device_inputs.push_back(inputs[input_index]->tensor(device_index));
    }
    device_thread->StartExecute(

  for (auto input : inputs) {
    forward_op_->inputs.push_back(input);
  }
  return absl::OkStatus();
}

absl::Status SetAttrString(AbstractOperation* op_, const char* attr_name,
                           const char* data, size_t length,
                           ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, StringPiece(data, length));

// GetSubSys - extracts subssystem info from given config string
func GetSubSys(s string) (subSys string, inputs []string, tgt string, e error) {
	tgt = Default
	if len(s) == 0 {
		return subSys, inputs, tgt, Errorf("input arguments cannot be empty")
	}
	inputs = strings.SplitN(s, KvSpaceSeparator, 2)

	subSystemValue := strings.SplitN(inputs[0], SubSystemSeparator, 2)
	subSys = subSystemValue[0]
	if !SubSystems.Contains(subSys) {

	for i = 0; i < 8; i++ {
		fi := FileInfo{
			TransitionTier:      inputs[0].tier,
			TransitionedObjName: inputs[0].remoteObjName,
			TransitionVersionID: inputs[0].remoteVersionID,
			TransitionStatus:    inputs[0].status,
		}
		ofi := fi
		if i&(1<<0) != 0 {
			ofi.TransitionTier = inputs[1].tier
		}
		if i&(1<<1) != 0 {
			ofi.TransitionedObjName = inputs[1].remoteObjName
		}
		if i&(1<<2) != 0 {

                               absl::Span<AbstractTensorHandle* const> inputs,
                               int input_index, bool use_function,
                               AbstractTensorHandle** numerical_grad) {
  vector<AbstractTensorHandle*> theta_inputs(inputs.size());
  for (int i{}; i < inputs.size(); ++i) {
    theta_inputs[i] = inputs[i];
  }

  AbstractTensorHandle* theta =