return builder.create<TF::ConstOp>(
      loc, DenseStringElementsAttr::get(
               tensor_type,
               SmallVector<StringRef>(str_values.begin(), str_values.end())));
}

// Creates a 1D string array constant for "tensor_names" input of `RestoreV2`
// op. The `ConstOp` will be created at `builder`'s current insertion point.

// before the NextIteration node is created.
string NextIterationName(const Scope& scope, int loop_var_idx) {
  string result;
  const string& prefix = scope.impl()->name();
  if (!prefix.empty()) strings::StrAppend(&result, prefix, "/");
  strings::StrAppend(&result, "NextIteration");
  if (loop_var_idx > 0) strings::StrAppend(&result, "_", loop_var_idx);
  return result;
}

        tf_op->emitError("malformed opdef");
        return failure();
      }
      if (tf_op->hasTrait<mlir::OpTrait::AttrSizedOperandSegments>()) {
        // Add derived "operand_segment_sizes" attr to the created operation.
        // TODO(b/146937733): Don't use <void> here.
        set_segment_sizes_attr(input_ranges, op_def->input_arg(),
                               mlir::OpTrait::AttrSizedOperandSegments<

                                       llvm::cl::init(""));

// A list of comma separated TF operators which are created by the user.
// This must be used with `-emit-select-tf-ops=true`.
// NOLINTNEXTLINE
opt<std::string> select_user_tf_ops(
    "select-user-tf-ops",
    llvm::cl::desc(
        "<list of custom tf ops created by the user (comma separated)>"),
    llvm::cl::init(""));

// NOLINTNEXTLINE

  if (!buffer_or.ok()) {
    done(buffer_or.status());
    return;
  }

  xla::PjRtBuffer* pjrt_buffer = (*buffer_or).get();
  if (use_pjrt_tensor_buffer_) {
    // Copy the newly created tensor with PjRtTensorBuffer to output device
    // tensor.
    absl::StatusOr<Tensor> t = MakeTensorFromPjRtBuffer(
        device_tensor->dtype(), device_tensor->shape(), std::move(*buffer_or));
    if (!t.ok()) {

  fetches.emplace_back(fetch_operand);

  auto builder = OpBuilder::atBlockTerminator(&main_graph_op.GetBody());
  builder.create<FetchOp>(main_graph_op.getLoc(), std::move(fetches));
}

// Creates a new Location for the initializer function. This creates a loc by
// attaching a to the initializer function's type so that it is identifiable.
Location CreateInitOpLoc(MLIRContext* ctx, func::FuncOp init_func_ops) {

  // Create return op for device computation region in the paralle_execute op
  Operation* after_op_r = builder.create<mlir::tf_device::ReturnOp>(
      new_device_cluster.getLoc(), device_results);

  builder.setInsertionPointToEnd(&parallel_execute_device_block);

  // Create return op for the new device cluster op
  builder.create<mlir::tf_device::ReturnOp>(device_cluster.getLoc(),

  func_input_arg = block->getArgument(0);
  return quantization_func;
}

// Post-actions after adding quantization logics. Post-actions include
// 1) Adding the created function in the symbol table
// 2) Creating a PartitionedCallOp in the main graph that calls the created
//    function.
TF::PartitionedCallOp FinalizeFunctionRegister(
    PatternRewriter& rewriter, Value input, Value output,

  for (Operation* subgraph_op : llvm::reverse(reverse_subgraph)) {
    // Create a deep copy of the subgraph ops' operands to the func op.
    stablehlo_func_op.getBody().begin()->push_back(subgraph_op->clone(mapper));
  }

  SmallVector<Value> result_values;
  for (const Value original_output_value : outputs) {
    // Use the mapped values in the newly created function that correspond to
    // outputs in the original function.

    "Resource ops decomposition did not converge";

// Decomposing resource ops should not take more than a few iterations (2-3) to
// converge as only a few patterns create new resource ops that can be further
// decomposed. The rest of the iterations are enough to clean up any dead ops
// created by decomposition.
constexpr int kMaxIterations = 20;

// Populates `reachable_functions` with all functions that can be reached from
// device cluster ops.