llvm::SmallVector<mlir::Type, 4> output_types(num_split, output_type);
  *split_op = builder->create<mlir::TF::SplitOp>(
      location, output_types, split_dimension_op.getOutput(), src_input);
  (*split_op)->setAttr(
      kNumSplitAttr,
      builder->getIntegerAttr(builder->getIntegerType(32), num_split));
  return mlir::success();
}

// Creates a tf::ConcatOp that merges `input` values in `concat_dimension`.

  Operation* parent_op = op->getParentOp();
  if (!parent_op) return false;
  return HasOutsideCompilationAncestor(parent_op);
}

Operation* ApplyXlaHostTransferAttr(Operation* op, OpBuilder& builder) {
  op->setAttr("_xla_has_host_transfer", builder.getBoolAttr(true));
  return op;
}

// Creates a tf._XlaSendFromHost or tf._XlaSendFromHostV2 op. If device ordinal
// is present, a tf._XlaSendFromHostV2 op is created instead.

  op->replaceAllUsesWith(flex_op);
  op->erase();
  return true;
}

// Sets the "no_fallback" attribute.
Value SetNoFallbackAttr(PatternRewriter &rewriter, Value val) {
  val.getDefiningOp()->setAttr(kNoFallbackAttr, rewriter.getUnitAttr());
  return val;
}

// Returns true if the attr is a float attribute and be equal to value.
static bool FloatValueEquals(const Attribute &attr, double value) {

    for (int i : llvm::seq<int>(0, metadata.result_devices.size())) {
      func_op.setResultAttr(
          i, kTFDeviceAttr,
          StringAttr::get(context, metadata.result_devices[i]));
    }

    func_op->setAttr(kHostAttr, StringAttr::get(context, host));
    func_op.setPublic();
    Block *block = func_op.addEntryBlock();

    // Clones and moves the operations into the function's body. And the cloned

                                     ModuleOp& module) {
  const auto ex_attr_value = module->getAttrOfType<IntegerAttr>(attr_name);
  if (ex_attr_value == nullptr) {
    module->setAttr(attr_name, attr_value);
    return success();
  }
  if (ex_attr_value == attr_value) {
    return success();
  }
  return op->emitOpError() << "module already contains an attribute "

  // LINT.ThenChange(../../../../core/common_runtime/inline_function_utils.cc)

  for (Block &block : inlinedBlocks) {
    block.walk([&](Operation *op) {
      if (op->getDialect() == call->getDialect())
        op->setAttr(device_id, node_device(op));
    });
  }
}

struct TFInlinerInterface : public DialectInlinerInterface {
  using DialectInlinerInterface::DialectInlinerInterface;

  // Check that there are no duplicated exported_names.
  DenseMap<StringRef, Operation *> exported_name_to_op;
  for (auto &op : module) {
    auto attr = op.getAttr(exported_names_ident);
    if (!attr) continue;
    // If this verifier is called before we verify the
    // 'tf_saved_model.exported_names' attribute, then it might be invalid.

class ConvertEmbeddedLookupFunc {
 public:
  explicit ConvertEmbeddedLookupFunc(func::FuncOp func) : func_(func) {}

  void RewriteFunc() {
    func_->setAttr(kTFImplements,
                   StringAttr::get(func_.getContext(), "embedding_lookup"));
    Value lookup = func_.getArgument(1);
    Value value = func_.getArgument(0);

    if (!preceding_qcast) return failure();

    auto new_qcast = rewriter.create<quantfork::QuantizeCastOp>(
        q_op.getLoc(), q_op.getType(), preceding_qcast.getArg());
    new_qcast->setAttr(kVolatileOpAttrName, rewriter.getUnitAttr());
    q_op->replaceAllUsesWith(new_qcast);
    return success();
  }
};

bool PrepareQuantizePass::ContainsQuantizeOps(func::FuncOp func) {

        // tensorflow/lite/kernels/fully_connected.cc. The kernels will handle
        // the asymmetric_quantize_inputs attribute in the builtin option.
        dynamic_range_quantized_user->setAttr(
            kAsymmetricQuantizeInputsAttr,
            BoolAttr::get(rewriter.getContext(), true));
        changed = true;
      }
    }
    return changed;
  }