int64_t arithmetic_count = arithmetic_count_op.GetArithmeticCount(op);
    auto attr =
        builder.getIntegerAttr(builder.getIntegerType(64), arithmetic_count);
    op->setAttr("_arithmetic_count", attr);
  });
}

}  // namespace

/// Creates an instance of the TensorFlow Lite dialect GetArithmeticCount
/// pass.

}

LogicalResult ConvertWhitespaceTokenizer(func::FuncOp func, llvm::StringRef api,
                                         FuncAttr attr) {
  func.eraseBody();
  func.addEntryBlock();
  func->setAttr(kTFImplements, attr);
  OpBuilder builder(func.getBody());
  std::string empty_option_buffer;
  auto op = builder.create<CustomOp>(
      func.getLoc(), func.getFunctionType().getResults(), func.getArguments(),

        /*config=*/builder.getStringAttr(""),
        /*config_proto=*/builder.getStringAttr(""),
        /*executor_type=*/builder.getStringAttr(""));
    call_op->setAttr("device", launch->getAttrOfType<StringAttr>("device"));
    launch.replaceAllUsesWith(call_op);
    launch.erase();
  });
}

}  // namespace

      "bad_consumers",
      b.getI32ArrayAttr(llvm::ArrayRef<int32_t>(
          versions.bad_consumers().data(),
          versions.bad_consumers().data() + versions.bad_consumers().size())));
  module->setAttr("tf.versions",
                  b.getDictionaryAttr(llvm::ArrayRef<mlir::NamedAttribute>(
                      {producer, min_consumer, bad_consumers})));
}

mlir::LogicalResult ExtractTfVersions(mlir::ModuleOp module,

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(MutatePass)

  void runOnOperation() override {
    mlir::OpBuilder builder(&getContext());
    getOperation()->setAttr("tfl.random_attr", builder.getUnitAttr());
  }
};

class AlwaysFailPass
    : public mlir::PassWrapper<AlwaysFailPass, mlir::OperationPass<>> {
 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(AlwaysFailPass)

  mutex_lock l(graph->mu);
  RecordMutation(graph, op, mutation_type);
}

void AddControlInput(TF_Graph* graph, TF_Operation* op, TF_Operation* input) {
  TF_AddOperationControlInput(graph, op, input);
}

void SetAttr(TF_Graph* graph, TF_Operation* op, const char* attr_name,
             TF_Buffer* attr_value_proto, TF_Status* status) {
  TF_SetAttr(graph, op, attr_name, attr_value_proto, status);
}

  }

  auto init = builder.create<func::FuncOp>(
      module.getLoc(), name,
      FunctionType::get(module.getContext(), /*inputs=*/{}, /*results=*/{}));

  // Make savedmodel verification happy.
  init->setAttr("tf_saved_model.exported_names",
                builder.getStrArrayAttr({name}));

  builder.setInsertionPointToStart(init.addEntryBlock());

  for (func::FuncOp func : tf_saved_model::GetInitializerFunctions(module)) {

    return;
  }

  auto function =
      symbol_table.lookup<func::FuncOp>(current_function_name.getValue());
  if (function) {
    function.setName(original_function_name);
  }

  call_op->setAttr(TF::kStablehloEntryFunctionAttrName,
                   FlatSymbolRefAttr::get(original_function_name));
}

void RestoreFunctionNamePass::runOnOperation() {
  ModuleOp module_op = getOperation();

      // device of the user operation.
      if (cloned_op_by_device.find(device_attr.getValue()) ==
          cloned_op_by_device.end()) {
        mlir::Operation *new_op = builder.clone(*op.getOperation());
        new_op->setAttr(kDeviceAttr, device_attr);
        cloned_op_by_device[device_attr.getValue()] = new_op;
      }
      // Update the user operation to use the result of the cloned ConstOp.

    }

    public void test_toString() {
        String tag = "tag", id = "id", css = "css", attrName = "attrName", attrValue = "attrValue";
        PrunedTag prunedtag = new PrunedTag(tag);
        prunedtag.setAttr(attrName, attrValue);
        prunedtag.setId(id);
        prunedtag.setCss(css);
        assertEquals("PrunedTag [tag=" + tag + ", id=" + id + ", css=" + css + ", attrName=" + attrName + ", attrValue=" + attrValue + "]",