* requires some work.
     */

    return Collector.of(
        ImmutableTableCollectorState<R, C, V>::new,
        (state, input) ->
            state.put(
                rowFunction.apply(input),
                columnFunction.apply(input),
                valueFunction.apply(input),
                mergeFunction),
        (s1, s2) -> s1.combine(s2, mergeFunction),
        state -> state.toTable());
  }

  static <

#include "tensorflow/core/graph/graph.h"

namespace tensorflow {

// This analyzes a TensorFlow graph to identify nodes which may have partially
// dead inputs (i.e. these nodes may have some dead inputs and some alive
// inputs).
//
// For example, the ADD node in the following graph
//
//      V0  PRED0    V1  PRED1
//       |    |       |    |
//       v    v       v    v
//       SWITCH       SWITCH

    protected String extractString(final String content) {
        String input = content.replaceAll("[\\r\\n]", " ");
        if (ignoreCommentTag) {
            input = input.replaceAll("<!--[^>]+-->", "");
        } else {
            input = input.replace("<!--", "").replace("-->", "");
        }
        final Matcher matcher = getTagPattern().matcher(input);
        final StringBuffer sb = new StringBuffer();

            executor.execute(() -> {
                while (true) {
                    String input;
                    try {
                        input = reader.readLine();
                    } catch (IOException e) {
                        throw UncheckedException.throwAsUncheckedException(e);
                    }
                    if (input == null) {
                        maybeClosed();
                        break;

//
// Args:
//  input_path: The path to the input TensorFlow SavedModel or MLIR module.
//  context: The MLIR context to use for parsing or creating the MLIR module.
//  exported_model_signatures: List of exported model signatures (strings) to
//    convert.
//  tag_names: List of tag names (strings) used for loading SavedModel.
//    Ignored for MLIR input.
//  input_arg_shapes_str:  A string representation of input argument shapes for

        return new org.apache.maven.model.Model(model);
    }

    @Override
    public org.apache.maven.model.Model read(InputStream input, Map<String, ?> options) throws IOException {
        Objects.requireNonNull(input, "input cannot be null");
        try (InputStream in = input) {
            org.apache.maven.api.model.Model model = read(null, in, null, options);
            return new org.apache.maven.model.Model(model);

OpView::OpView(OpSpec op)
    : op_(op),
      input_args_(op_.Inputs().begin(), op_.Inputs().end()),
      output_args_(op_.Outputs().begin(), op_.Outputs().end()),
      argument_attrs_(op_.Attributes().begin(), op_.Attributes().end()) {
  // Initialize function arguments
  all_arguments_.push_back(OpArgumentView("AbstractContext*", "ctx"));
  for (const auto& arg : op_.Inputs()) {
    all_arguments_.push_back(OpArgumentView(arg));
  }

    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,

   *
   * @param input the input bytes
   * @param offset the offset into the array at which to start
   * @return a long of a concatenated 8 bytes
   */
  static long load64(byte[] input, int offset) {
    // We don't want this in production code as this is the most critical part of the loop.
    assert input.length >= offset + 8;
    // Delegates to the fast (unsafe) version or the fallback.

            ImmutableSortedMap.of("input", inputSnapshot),
            ImmutableSortedMap.of(),
            ImmutableSortedMap.of("input-files", inputFilesFingerprint),
            ImmutableSet.of()
        )

        1 * delegate.execute(work, _ as IdentityContext) >> { UnitOfWork work, IdentityContext delegateContext ->
            assert delegateContext.inputProperties as Map == ["input": inputSnapshot]