time.Sleep(time.Minute)
			}
		}()
	}
	// Now apply kube reserved and system reserved limits if required.
	if nc.EnforceNodeAllocatable.Has(kubetypes.SystemReservedEnforcementKey) {
		klog.V(2).InfoS("Enforcing system reserved on cgroup", "cgroupName", nc.SystemReservedCgroupName, "limits", nc.SystemReserved)

inline DenseElementsAttr GetShape(Value output_val, bool truncate = false) {
  auto output_shape = output_val.getType().dyn_cast<ShapedType>().getShape();

  SmallVector<int32_t> shape;
  shape.reserve(output_shape.size());

  bool needs_truncation = true;
  for (size_t dim_idx = 0; dim_idx < output_shape.size(); ++dim_idx) {
    int64_t dim = output_shape[dim_idx];
    if (truncate && needs_truncation && dim == 1) {

    }
    return success();
  }
  void rewrite(TF::_XlaHostComputeMlirOp op,
               PatternRewriter& rewriter) const override {
    llvm::SmallVector<Attribute> shape_attrs;
    shape_attrs.reserve(op.getNumResults());
    for (Type ty : op.getResultTypes()) {
      shape_attrs.push_back(TF::ShapeAttr::get(rewriter.getContext(),
                                               mlir::cast<ShapedType>(ty)));
    }

}

void TF_LockVariableInfos(TF_VariableInfo** vars, int num_vars,
                          TF_Status* status) {
  std::vector<tensorflow::VariableInfo*> variable_ptrs;
  variable_ptrs.reserve(num_vars);
  for (int i = 0; i < num_vars; ++i) {
    variable_ptrs.push_back(&(vars[i]->var_info));
  }
  absl::Status cc_status = LockVariables(absl::MakeSpan(variable_ptrs));
  status->status = cc_status;
}

  // supports importing graphs w/ unregistered ops natively.
  GraphDef pruned_graph_def;
  if (specs.prune_unused_nodes) {
    std::vector<std::string> terminal_nodes;
    terminal_nodes.reserve(specs.outputs.size() + specs.inputs.size());
    for (const auto& output : specs.outputs) {
      terminal_nodes.push_back(std::string(ParseTensorName(output).node()));
    }

  }
  return dnums;
}

DenseIntElementsAttr ConvertToDenseElementsAttr(ArrayAttr array_attr,
                                                PatternRewriter &rewriter) {
  SmallVector<int64_t> array;
  array.reserve(array_attr.size());
  for (auto elem : array_attr.getAsRange<IntegerAttr>()) {
    array.push_back(elem.getInt());
  }
  return DenseIntElementsAttr::get(

    // Note that we take the address of the elements in `all_input_tensors`
    // 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();

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        llvm::dyn_cast<tf_device::LaunchOp>(compile->getParentOp());
    if (!compile_launch || !compile_launch.WrapsSingleOp()) return;

    llvm::SmallVector<tf_device::LaunchOp, 4> execute_launches;
    execute_launches.reserve(compile_launch.getNumResults() - 1);
    for (Value program_result :
         llvm::drop_begin(compile_launch.getResults(), 1)) {
      if (!program_result.hasOneUse()) return;

		if err != nil {
			return nil, nil, err
		}
		if m != orig {
			m.close()
		}
		m = newM
		v, headOff, head, ok = m.lookup(name)
	}
	if v != nil {
		return v, nil, nil
	}

	// Reserve space for new record.
	// We are competing against other programs using the same file,
	// so we use a compare-and-swap on the allocation limit in the header.
	var start, end uint32
	for {