void TF_AddInput(TF_OperationDescription* desc, TF_Output input) {
  desc->node_builder.Input(&input.oper->node, input.index);
}

void TF_AddInputList(TF_OperationDescription* desc, const TF_Output* inputs,
                     int num_inputs) {
  std::vector<NodeBuilder::NodeOut> input_list;
  input_list.reserve(num_inputs);
  for (int i = 0; i < num_inputs; ++i) {
    input_list.emplace_back(&inputs[i].oper->node, inputs[i].index);
  }

    std::unordered_map<const Node*, std::vector<int>>* input_nodes)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  input_tensors->reserve(ninputs);
  for (int i = 0; i < ninputs; ++i) {
    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),

//          automatic skipping of dummy input operations is performed.
//  ninputs - number of elements in `inputs` array
//  inputs - array of TF_Outputs that specify the inputs to the function.
//           If `ninputs` is zero (the function takes no inputs), `inputs`
//           can be null. The names used for function inputs are normalized

    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();
      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);

      grad_inputs[0] = TF_Output{grad_inputs_op, 0};
      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, grad_inputs, s_, grad_outputs);
    } else {
      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, nullptr, s_, grad_outputs);
    }
  }

  void BuildErrorGraph(TF_Output* inputs, TF_Output* outputs) {

      TF_GraphToFunction(
          /* fn_body */ body.get(), /* fn_name */ function_name,
          /* append_hash_to_fn_name */ 0, /* num_opers */ 2,
          /* opers */ operations, /* ninputs */ 1, /* inputs */ &x,
          /* noutputs */ 1, /* outputs */ &y, /* output_names */ &output_name,
          /* opts */ nullptr, /* description */ "", /* status */ status),
      TF_DeleteFunction);

	"strings"
	"text/scanner"

	"cmd/asm/internal/flags"
	"cmd/internal/objabi"
	"cmd/internal/src"
)

// Input is the main input: a stack of readers and some macro definitions.
// It also handles #include processing (by pushing onto the input stack)
// and parses and instantiates macro definitions.
type Input struct {
	Stack
	includes        []string
	beginningOfLine bool
	ifdefStack      []bool

  int num_inputs = TFE_OpGetFlatInputCount(other, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  for (int input_index = 0; input_index < num_inputs; ++input_index) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(other, input_index, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_OpAddInput(ret, input, status);

/**
 * A future whose value is derived from a collection of input futures.
 *
 * @param <InputT> the type of the individual inputs
 * @param <OutputT> the type of the output (i.e. this) future
 */
@GwtCompatible
@ElementTypesAreNonnullByDefault
abstract class AggregateFuture<InputT extends @Nullable Object, OutputT extends @Nullable Object>
    extends AggregateFutureState<OutputT> {

public class ProtwordsItem extends DictionaryItem {
    private final String input;

    private String newInput;

    public ProtwordsItem(final long id, final String input) {
        this.id = id;
        this.input = input;

        if (id == 0) {
            // create
            newInput = input;
        }
    }

    public String getNewInput() {
        return newInput;
    }