assert(metadata_str && "Missing compilation metadata");
  tensorflow::tpu::TPUCompileMetadataProto metadata;
  metadata.ParseFromString(std::string(metadata_str.getValue()));
  int64_t num_replicas = replicate.getN();
  // Find the formattable operands of `execute`, which must be mirrored
  // variables (arguments of `replicate`), and must be pass-throughs from while
  // operands.

* Prüfen und lesen Sie den Code, fragen Sie sich, ob er Sinn macht, **führen Sie ihn lokal aus** und testen Sie, ob er das Problem tatsächlich löst.

* Schreiben Sie dann einen **Kommentar** und berichten, dass Sie das getan haben. So weiß ich, dass Sie ihn wirklich überprüft haben.

!!! info
    Leider kann ich PRs, nur weil sie von Mehreren gutgeheißen wurden, nicht einfach vertrauen.

            stream(parameter.getSuggestFields()).of(stream -> stream.forEach(builder::addField));
            roleQueryHelper.build(SearchRequestType.SUGGEST).stream().forEach(builder::addRole);
            builder.setSize(parameter.getNum());
            stream(langs).of(stream -> stream.forEach(builder::addLang));

            stream(parameter.getTags()).of(stream -> stream.forEach(builder::addTag));

    auto min_max_type = GetUniformQuantizedType(
        op, op.getMin().getType(), op.getScales(), op.getZeroPoints(),
        /*expressed_type=*/rewriter.getF32Type(), op.getQuantizationMinVal(),
        op.getQuantizationMaxVal(), op.getQuantizationAxis(), rewriter);
    if (failed(min_max_type)) {
      return failure();
    }
    auto min_or = CreateConstantOrConvertOp(op, adaptor.getMin(), *min_max_type,

	require.NoError(t, c.rotateLogs(ctx))

	// Start a routine that can monitor the queue and shutdown the queue to trigger the retrun from the processQueueItems
	// Keeping the monitor duration smaller in order to keep the unwanted delay in the test to a minimal.
	go func() {
		pollTimeoutCtx, cancel := context.WithTimeout(ctx, 10*time.Second)

      auto it = buffer_to_size->find(addn.getOperand(0));
      if (it != buffer_to_size->end()) {
        addn.getSum().setType(
            mlir::cast<TensorType>(addn.getOperand(0).getType()));
        auto size = it->getSecond();
        (*buffer_to_size)[addn.getSum()] = size;
      }
    } else if (auto zeros = llvm::dyn_cast<TF::ZerosLikeOp>(&op)) {
      if (buffer_to_size->count(zeros.getX()) > 0) {

      continue;

    const auto inputs = partitioned_input.getInputs();
    const bool packed_input = partitioned_input.getIsPacked();
    int num_cores_per_replica = partitioned_input.getN();
    if (num_cores_per_replica_attr) {
      num_cores_per_replica = num_cores_per_replica_attr.getInt();
    } else if (packed_input) {
      return partitioned_input->emitOpError()

  if (!status->status.ok()) return;
  auto run_metadata = context->ExportRunMetadata();
  status->status = MessageToBuffer(*run_metadata, buf);
}

namespace {
TFE_Op* GetFunc(TFE_Context* ctx, const tensorflow::NameAttrList& func,
                TF_Status* status) {
  TFE_Op* func_op = TFE_NewOp(ctx, func.name().data(), status);
  for (const auto& attr : func.attr()) {

    Operation* op, PatternRewriter& rewriter) const {
  auto tf_unpack_op = cast<TF::UnpackOp>(op);

  auto input = tf_unpack_op.getValue();
  auto num = rewriter.getI32IntegerAttr(tf_unpack_op.getNum());
  // Axis can be negative.
  auto axis = rewriter.getI32IntegerAttr(tf_unpack_op.getAxis());

  rewriter.replaceOpWithNewOp<UnpackOp>(op, tf_unpack_op.getOutput().getTypes(),

n function(n){var o={};function r(t){if(o[t])return o[t].exports;var e=o[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,r),e.l=!0,e.exports}return r.m=n,r.c=o,r.d=function(t,e,n){r.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:n})},r.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof...