DCHECK_EQ(def->getParentOp(), graph);
      try_update_current_candidate(def);
    }

    // Check island data operands.
    island.walk([&](Operation* op) {
      for (Value input : op->getOperands()) {
        Operation* def = input.getDefiningOp();
        if (!def || def->getParentOp() != graph) continue;

        try_update_current_candidate(def);
      }
    });
  }

  // Replace replicate terminator with YieldOp.
  builder.setInsertionPoint(&terminator);
  builder.create<tf_executor::YieldOp>(terminator.getLoc(),
                                       terminator.getOperands());
  terminator.erase();

  builder.setInsertionPoint(island_op);
  IRMapping mapping;
  for (int i : llvm::seq<int>(0, num_replicas)) {
    // Create new island for replica.

      // inputs.
      SmallVector<Value, 4> inputs;
      inputs.reserve(candidate_op->getNumOperands());
      for (auto operand : candidate_op->getOperands()) {
        Type operand_type = operand.getType();
        if (mlir::isa<NoneType>(operand_type)) {
          inputs.push_back(operand);
          continue;
        }

        auto ele_type =

  mhlo::ReturnOp return_op = dyn_cast<mhlo::ReturnOp>(body.back());
  if (!return_op) return failure();
  if (return_op.getNumOperands() != 1) return failure();
  if (return_op.getOperands().front() != body.getArgument(1)) return failure();
  return success();
}

bool NeedsReformatTypeAndPermutation(int batch_dim, int feature_dim,
                                     int spatial_dim_start,

        ReadAttr(flex_buffer_map, &builder, custom_op.getCustomCode().str());
    OperationState op_state(custom_op.getLoc(),
                            custom_op.getCustomCode().str());
    op_state.addOperands(custom_op.getOperands());
    llvm::SmallVector<mlir::Type, 4> output_tys;
    for (int i = 0; i < custom_op.getNumResults(); i++) {
      output_tys.push_back(custom_op.getType(i));
    }
    op_state.addTypes(output_tys);

  return wrapped_op.getNumResults() == terminator->getNumOperands() &&
         std::equal(wrapped_op.getResults().begin(),
                    wrapped_op.getResults().end(),
                    terminator->getOperands().begin());
}
}  // end anonymous namespace

TensorFlowDeviceDialect::TensorFlowDeviceDialect(MLIRContext* context)
    : Dialect(/*name=*/"tf_device", context,

      // inputs.
      SmallVector<Value, 4> inputs;
      inputs.reserve(quantizing_op->getNumOperands());
      for (const auto& operand : quantizing_op->getOperands()) {
        Type operand_type = operand.getType();
        if (operand_type.isa<NoneType>()) {
          inputs.push_back(operand);
          continue;
        }

    const std::string tf_op_full_name = llvm::Twine("tf.", tf_op_name).str();

    // Create the TF op
    OperationState op_state(op.getLoc(), tf_op_full_name);
    op_state.addOperands(op.getOperands());
    op_state.addTypes(op.getResultTypes());

    SmallVector<NamedAttribute, 2> attrs;
    std::string parsed_op_name;
    tensorflow::NodeDef node_def;

    return mlir::isa<mlir::TF::StringType>(type.getElementType());
  });
  return has_string;
}

bool HasStringOperand(Operation& op) {
  for (auto operand : op.getOperands()) {
    auto operand_type = getElementTypeOrSelf(operand);
    if (IsStringType(operand_type)) return true;
  }
  return false;
}

bool HasStringResult(Operation& op) {

              1, mlir::stablehlo::PrecisionAttr::get(
                     ctx_.get(), mlir::stablehlo::Precision::DEFAULT)))),
  };
  const SmallVector<Value> operands(dot_general_op->getOperands());
  const SmallVector<Value> results(dot_general_op->getResults());
  Operation* lifted_op =
      LiftAsFunctionCall(builder_, dot_general_op->getLoc(),
                         FunctionCallOpType::TFXlaCallModuleOp,