//  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
//           names of the operations (usually placeholders) pointed to by

  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,
                /*targets*/ nullptr, /*ntargets*/ 0,

      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) {

      for (List<Integer> contents : SAMPLE_INPUTS) {
        for (int toRemove = 0; toRemove < 10; toRemove++) {
          assertEquals(
              contents.contains(toRemove) && EVEN.apply(toRemove),
              filter(createUnfiltered(contents), EVEN).remove(toRemove));
        }
      }
    }

    public void testContains() {
      for (List<Integer> contents : SAMPLE_INPUTS) {
        for (int i = 0; i < 10; i++) {

    private final String[] inputs;

    private final String output;

    private String[] newInputs;

    private String newOutput;

    public CharMappingItem(final long id, final String[] inputs, final String output) {
        this.id = id;
        this.inputs = inputs;
        this.output = output == null ? null : output.replace("\n", " ");
        Arrays.sort(inputs);

        if (id == 0) {

      for (List<Integer> contents : SAMPLE_INPUTS) {
        for (int toRemove = 0; toRemove < 10; toRemove++) {
          assertEquals(
              contents.contains(toRemove) && EVEN.apply(toRemove),
              filter(createUnfiltered(contents), EVEN).remove(toRemove));
        }
      }
    }

    public void testContains() {
      for (List<Integer> contents : SAMPLE_INPUTS) {
        for (int i = 0; i < 10; i++) {

import java.io.StringWriter;
import java.util.Random;

/** Benchmark for {@code BaseEncoding} performance. */
public class BaseEncodingBenchmark {
  private static final int INPUTS_COUNT = 0x1000;
  private static final int INPUTS_MASK = 0xFFF;

  enum EncodingOption {
    BASE64(BaseEncoding.base64()),
    BASE64_URL(BaseEncoding.base64Url()),
    BASE32(BaseEncoding.base32()),
    BASE32_HEX(BaseEncoding.base32Hex()),

        with:
          full_repository: ${{ github.event.inputs.full_repository }}
          head_ref: ${{ github.event.inputs.head_ref }}
          base_ref: ${{ github.event.inputs.base_ref }}
          client_payload: ${{ github.event.inputs.client_payload }}
          installation_id: ${{ github.event.inputs.installation_id }}

                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Sub(ctx, inputs[0], inputs[1], &outputs[0], "Sub");
}

Status MulModel(AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Mul(ctx, inputs[0], inputs[1], &outputs[0], "Mul");
}

                    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);