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

  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) {

  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()) {

  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,

        pm.addPass(mlir::quant::CreateUnfreezeConstantsPass());
      },
      ctx, module_op));

  if (const absl::Status create_dir_status =
          Env::Default()->CreateDir(std::string(checkpoint_dir));
      !create_dir_status.ok()) {
    LOG(ERROR) << "Failed to create checkpoint directory at: "
               << checkpoint_dir;
    return create_dir_status;
  }

  TF_ASSIGN_OR_RETURN(const auto unused_variable_names,

#include "tensorflow/core/platform/types.h"

struct MyCustomKernel {
  bool created;
  bool compute_called;
};

static bool delete_called = false;
static bool async_kernel_done = false;

static void* MyCreateFunc(TF_OpKernelConstruction* ctx) {
  struct MyCustomKernel* s = new struct MyCustomKernel;
  s->created = true;
  s->compute_called = false;

  // Exercise attribute reads.
  TF_DataType type;

// creates a new session_initializer op and returns it.
SessionInitializerOp GetOrCreateSessionInitializerOp(ModuleOp module_op) {
  SessionInitializerOp session_init_op = GetSessionInitializerOp(module_op);

  // Create one if it doesn't exist.
  if (!session_init_op) {
    OpBuilder builder(&module_op.getBodyRegion());

    session_init_op = builder.create<SessionInitializerOp>(