// Setting a low transfer speed ensures that stream discarding will time out.
    val builder =
      MockResponse.Builder()
        .throttleBody(6, 1, TimeUnit.SECONDS)
    transferKind.setBody(builder, "ABCDE\nFGHIJKLMNOPQRSTUVWXYZ", 1024)
    server.enqueue(builder.build())
    server.enqueue(
      MockResponse.Builder()
        .body("Request #2")
        .build(),
    )
    val response1 = get(server.url("/"))

    path: String,
    vararg headers: String?,
  ): RecordedResponse {
    val builder = Request.Builder()
    builder.url(server.url(path))
    var i = 0
    val size = headers.size
    while (i < size) {
      builder.addHeader(headers[i]!!, headers[i + 1]!!)
      i += 2
    }
    return executeSynchronously(builder.build())
  }

  private fun executeSynchronously(request: Request): RecordedResponse {

                                            mlir::Builder builder);

  // Returns the inferred output type at index `idx` of the `node` in the
  // context.
  absl::StatusOr<mlir::Type> InferOutputType(const Node& node, int idx,
                                             mlir::Builder builder);

  // Convert deferred TF functions to the MLIR representation.

	pack(b *Builder, a *Action, afile string, ofiles []string) error
	// ld runs the linker to create an executable starting at mainpkg.
	ld(b *Builder, root *Action, targetPath, importcfg, mainpkg string) error
	// ldShared runs the linker to create a shared library containing the pkgs built by toplevelactions
	ldShared(b *Builder, root *Action, toplevelactions []*Action, targetPath, importcfg string, allactions []*Action) error

      RankedTensorType::get(static_cast<int32_t>(1), builder.getI32Type());
  auto out_segids_cst = builder.create<TF::ConstOp>(
      builder.getI32TensorAttr(flattened_out_segids));
  auto contracting_segids_cst = builder.create<TF::ConstOp>(
      builder.getI32TensorAttr(flattened_contracting_segids));
  auto num_segids_tensor =
      builder.create<TF::ConstOp>(builder.getI32IntegerAttr(1));

   */
  LocalCache(
      CacheBuilder<? super K, ? super V> builder, @CheckForNull CacheLoader<? super K, V> loader) {
    concurrencyLevel = Math.min(builder.getConcurrencyLevel(), MAX_SEGMENTS);

    keyStrength = builder.getKeyStrength();
    valueStrength = builder.getValueStrength();

    keyEquivalence = builder.getKeyEquivalence();
    valueEquivalence = builder.getValueEquivalence();

   */
  LocalCache(
      CacheBuilder<? super K, ? super V> builder, @CheckForNull CacheLoader<? super K, V> loader) {
    concurrencyLevel = Math.min(builder.getConcurrencyLevel(), MAX_SEGMENTS);

    keyStrength = builder.getKeyStrength();
    valueStrength = builder.getValueStrength();

    keyEquivalence = builder.getKeyEquivalence();
    valueEquivalence = builder.getValueEquivalence();

                                               Builder *builder) {
  RankedTensorType ty = mlir::RankedTensorType::get(
      {static_cast<int32_t>(values.size())}, builder->getIntegerType(32));
  return DenseIntElementsAttr::get(ty, values);
}

DenseIntElementsAttr GetI64ElementsAttr(ArrayRef<int64_t> values,
                                        Builder *builder) {
  RankedTensorType ty = RankedTensorType::get(

  }

  // Otherwise, build a new concatenation op with non-empty values.
  mlir::OpBuilder builder(getOperation());
  auto new_concat = builder.create<TFL::ConcatenationOp>(
      getLoc(), getType(), non_empty_values,
      builder.getIntegerAttr(builder.getIntegerType(32), getAxis()),
      builder.getStringAttr(getFusedActivationFunction()));
  return new_concat.getResult();
}

      op.getOperation());
}

void NotEqualOp::build(OpBuilder &builder, OperationState &result, Value x,
                       Value y, BoolAttr incompatible_shape_error) {
  auto result_type = DeduceEqualCmpOpType(&builder, result.location, x, y,
                                          incompatible_shape_error);
  return build(builder, result, result_type, x, y, incompatible_shape_error);
}