Type output_type = bmm_op.getResult().getType();
    auto no_input = rewriter.create<TFL::NoValueOp>(
        bmm_op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr());
    auto fc_op = rewriter.create<TFL::FullyConnectedOp>(
        bmm_op->getLoc(), ArrayRef<Type>{output_type},
        /*input=*/output_lhs, /*filter=*/output_rhs, /*bias=*/no_input,

  builder.create<mlir::TF::YieldOp>(if_region.getLoc(),
                                    /*operands=*/ArrayRef<Value>{});

  // Create empty else branch region.
  auto& else_branch = host_side_if.getElseBranch();
  else_branch.push_back(new Block);
  builder.setInsertionPointToEnd(&else_branch.front());
  builder.create<mlir::TF::YieldOp>(if_region.getLoc(),

      replacer.create<mlir::func::ReturnOp>(yield_op.getLoc(),
                                            yield_op.getOperands());
      yield_op.erase();
    }

    // Remap the captured operands in the (former) island block with newly
    // created entry block arguments in the function body.
    {
      Block &entry_block = outlined_func.getBody().front();
      auto loc = outlined_func.getLoc();
      for (Value operand : operands) {

      }
    } else {
      bias = rewriter
                 .create<AddOp>(add_op.getLoc(), bias, constant_val,
                                rewriter.getStringAttr("NONE"))
                 .getOutput();
    }

    auto fc = rewriter.create<TFL::FullyConnectedOp>(
        FusedLoc::get(fc_op.getContext(), {fc_op.getLoc(), add_op.getLoc()}),
        add_op.getType(),
        /*input=*/fc_op.getInput(),

    Value scalar_zero = CreateI32SplatConst(op.getLoc(), rewriter, {}, 0);
    Type shape_dtype = getElementTypeOrSelf(op.getElementShape().getType());
    Value num_elements = operands[1];
    IntegerAttr attr;
    if (matchPattern(num_elements, m_Constant(&attr))) {
      return CreateI32SplatConst(op.getLoc(), rewriter, {1}, attr.getInt());
    }

    }
    operand = rewriter.create<mhlo::BitcastConvertOp>(op->getLoc(),
                                                      *output_type, operand);

    Value res_min_clipped = rewriter.create<chlo::BroadcastMaxOp>(
        op->getLoc(), *output_type, operand, *min_or, broadcast_dims);
    Value res_max_clipped = rewriter.create<chlo::BroadcastMinOp>(
        op->getLoc(), *output_type, res_min_clipped, *max_or, broadcast_dims);

  }

  auto anonymous_iterator = builder.create<AnonymousIteratorV3Op>(
      reduce_dataset.getLoc(),
      RankedTensorType::get({}, builder.getType<ResourceType>()),
      /*output_types=*/builder.getArrayAttr(type_attrs),
      /*shape_types=*/builder.getArrayAttr(shape_attrs));
  builder.create<MakeIteratorOp>(reduce_dataset.getLoc(),
                                 reduce_dataset.getInputDataset(),

        rewriter.create<arith::ConstantOp>(op->getLoc(), type, begin_attr);
    auto end_attr = DenseElementsAttr::get<int32_t>(type, padded_end);
    auto end_op =
        rewriter.create<arith::ConstantOp>(op->getLoc(), type, end_attr);
    auto stride_attr = DenseElementsAttr::get<int32_t>(type, padded_stride);
    auto stride_op =
        rewriter.create<arith::ConstantOp>(op->getLoc(), type, stride_attr);

  OpBuilder builder(func.getBody());
  std::string empty_option_buffer;
  auto op = builder.create<CustomOp>(
      func.getLoc(), func.getFunctionType().getResults(), func.getArguments(),
      api, CustomOption(&builder, empty_option_buffer));
  builder.create<func::ReturnOp>(func.getLoc(), op.getResults());
  return success();
}

LogicalResult VerifyNgrams(func::FuncOp func) {

  assert(caller_region.empty() &&
         "Expected empty region for newly created ops");
  OpBuilder builder(caller_region);
  Block* entry = builder.createBlock(&caller_region);

  auto loc = op->getLoc();
  if (use_region_args) {
    auto inputs = func.getFunctionType().getInputs();
    entry->addArguments(inputs, SmallVector<Location>(inputs.size(), loc));
    args = entry->getArguments();
  }