<default-constraints vsize-policy="1" hsize-policy="6" anchor="0" fill="1" />
      </item>
      <item class="com.intellij.uiDesigner.VSpacer" tooltip-text="Vertical Spacer" icon="/com/intellij/uiDesigner/icons/vspacer.png" removable="false" auto-create-binding="false" can-attach-label="false">

  Operation* result_user = *result.getUsers().begin();
  auto assign_var = dyn_cast<TF::AssignVariableOp>(result_user);
  if (!assign_var) return resource;

  auto handle = assign_var.getResource();
  // Skip result if cluster writes to the same variable via multiple results.
  for (Operation* handle_user : handle.getUsers()) {
    if (handle_user == assign_var) continue;
    auto assign_var_user = dyn_cast<TF::AssignVariableOp>(handle_user);

ResourceHandle GetResourceHandle(Operation *op) {
  llvm::StringRef device;
  if (auto attr = op->getAttrOfType<StringAttr>("device")) {
    device = attr.getValue();
  }

  llvm::StringRef container;
  if (auto attr = op->getAttrOfType<StringAttr>("container")) {
    container = attr.getValue();
  }

  llvm::StringRef shared_name;
  if (auto attr = op->getAttrOfType<StringAttr>("shared_name")) {
    shared_name = attr.getValue();

    return failure();
  }

  auto session_init_func = GetOrCreateSessionInitFunc(module);
  OpBuilder builder(session_init_func.getContext());

  for (auto var_and_tensor : llvm::zip(var_ops, resource_tensors_or.value())) {
    auto& var_op = std::get<0>(var_and_tensor);
    auto& resource_tensor = std::get<1>(var_and_tensor);
    if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {

  out = std::get<2>(flattended_labels);

  auto lhs_map_or = EquationToMap(lhs);
  if (!lhs_map_or.has_value()) return std::nullopt;
  auto lhs_map = lhs_map_or.value();

  auto out_map_or = EquationToMap(out);
  if (!out_map_or.has_value()) return std::nullopt;
  auto out_map = out_map_or.value();

  EinsumDimensionNumbers dnums;
  for (int64_t i = 0; i < lhs.size(); ++i) {
    auto out_index = out_map.find(lhs[i]);

    auto &body_block = while_op.getBody().front();
    auto &cond_block = while_op.getCond().front();
    bool changed = false;
    for (int op_idx : llvm::seq<int>(0, while_op.getNumOperands())) {
      auto body_arg = body_block.getArgument(op_idx);
      auto cond_arg = cond_block.getArgument(op_idx);
      auto while_operand = while_op.getOperand(op_idx);

  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelContext*>(ctx);
  int start = -1, stop = -1;
  auto status = cc_ctx->op_kernel().InputRange(name, &start, &stop);
  args->start = start;
  args->stop = stop;
  tensorflow::Set_TF_Status_from_Status(args->status, status);
}

TF_DataType TF_InputDatatype(TF_OpKernelContext* ctx, int index) {

};

bool ShouldStripAttr(NamedAttribute &namedAttr) {
  auto name = namedAttr.getName().strref();
  return name.starts_with("tf_saved_model.");
}

void StripModule(Operation *module) {
  auto stripAttrs = llvm::to_vector<4>(llvm::make_filter_range(
      module->getAttrs(),
      [](NamedAttribute namedAttr) { return ShouldStripAttr(namedAttr); }));
  for (auto namedAttr : stripAttrs) {
    module->removeAttr(namedAttr.getName());

    auto guard = OpBuilder::InsertionGuard(*rewriter);
    auto inputs = branch_func.getFunctionType().getInputs();
    Block *block = rewriter->createBlock(
        &branch_func.getBody(), branch_func.begin(), inputs,
        SmallVector<Location>(inputs.size(), branch_func.getLoc()));

    auto input_shape = block->getArgument(1);
    auto size_diff = block->getArgument(2);

                .run()

        then: "the JDK is auto-provisioned again and its files, even though they are already there don't trigger an error, they just get overwritten"
        markerFile.exists()
    }

    def "issue warning on using auto-provisioned toolchain with no configured repositories"() {