if (!op.isPrivate()) return;

    auto call = llvm::dyn_cast<CallableOpInterface>(op.getOperation());
    if (!call) return;
    Region* region = call.getCallableRegion();
    if (!region) return;  // happens e.g. for external functions

    auto func = llvm::dyn_cast<FunctionOpInterface>(op.getOperation());
    if (!func || do_not_touch.count(func)) return;
    llvm::BitVector unused_args(func.getNumArguments());

      input_shapes_ = input_shapes_vec;
    }

    auto failure_or_converged = InferModuleShape(
        getOperation(), max_iterations_, /*ops_to_skip=*/{}, input_shapes_);
    if (failed(failure_or_converged)) return signalPassFailure();
    if (!failure_or_converged.value()) {
      getOperation().emitError()
          << "shape inference pass did not reach convergence after "
          << max_iterations_;

  func::FuncOp callee = from.lookup<func::FuncOp>(symbol_ref.getValue());
  callee.getOperation()->getBlock()->getOperations().remove(
      callee.getOperation());
  to.insert(callee);
}

void TPUBridgeExecutorIslandOutlining::runOnOperation() {
  MLIRContext *ctx = &getContext();

  SymbolTable symbol_table(getOperation());
  if (Operation *nested_module = symbol_table.lookup(kNestedModule)) {

  // Move all inner ops of the launch to the block containing the launch.
  auto body = launch.GetBody().without_terminator();
  Operation* launch_op = launch.getOperation();
  launch_op->getBlock()->getOperations().splice(
      launch_op->getIterator(), launch.GetBody().getOperations(), body.begin(),
      body.end());

  launch.erase();

  return success();
}

void LaunchToDeviceAttributePass::runOnOperation() {

  int64_t total_size_transferred = 0;
  for (auto input : to_graph.getOperands()) {
    Operation* input_op = input.getDefiningOp();
    if (input_op && input_op == from_graph.getOperation()) {
      auto input_type =
          mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
      if (input_type == nullptr || !input_type.hasStaticShape()) continue;

 public:
  using OpRewritePattern<SrcOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(SrcOp srcop,
                                PatternRewriter &rewriter) const final {
    Operation *op = srcop.getOperation();
    bool allTypesFp = true;
    bool allTypesQuantizedOrInt = true;
    for (auto operand : op->getOperands()) {
      ShapedType type = mlir::dyn_cast<ShapedType>(operand.getType());
      if (!type) continue;

    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();

  llvm::SmallVector<TF::TPUReplicateMetadataOp, 4> metadata;
  getOperation()->walk(
      [&metadata](TF::TPUReplicateMetadataOp op) { metadata.push_back(op); });

  IntegerAttr num_cores_per_replica;
  if (metadata.size() == 1) {
    num_cores_per_replica = metadata.front().getNumCoresPerReplicaAttr();
  }

  auto result = getOperation()->walk([&num_cores_per_replica](Operation* op) {

  target.addLegalDialect<TFL::TensorFlowLiteDialect>();
  target.addLegalDialect<arith::ArithDialect>();

  if (failed(applyPartialConversion(getOperation(), target,
                                    std::move(patterns)))) {
    getOperation().emitError("Composite lowering pass failed.");
    signalPassFailure();
  }
}

}  // namespace

// Creates an instance of the pass.

                        .getOperation();
  tpu_copy_with_dynamic_shape_op->moveBefore(return_op);
  return success();
}

// Update all the usage of tf_device.return op with launch op result.
void UpdateReturnOpResultWithLaunchOpResult(tf_device::LaunchOp* launch_op) {
  auto operand_not_in_launch = [&](OpOperand& operand) {
    return !launch_op->getOperation()->isProperAncestor(operand.getOwner());
  };