auto resource_operand = assign_var_op.getOperand(0);
  auto var_handle_op =
      llvm::dyn_cast<mlir::TF::VarHandleOp>(resource_operand.getDefiningOp());
  if (!var_handle_op) {
    assign_var_op->emitRemark(
        "Operand idx 0 is not a tf.VarHandleOp. The initializing tensor is not "
        "saved to checkpoint.");
    return "";
  }

// CHECK: func.func private @tf_quant__save(%[[ARG:.*]]: tensor<!tf_type.string>)
// CHECK: %[[VAR_HANDLE_0:.*]] = "tf.VarHandleOp"()
// CHECK-SAME: {{.*shared_name = "var_0".*}}
// CHECK: %[[VAR_HANDLE_1:.*]] = "tf.VarHandleOp"()
// CHECK-SAME: {{.*shared_name = "var_1".*}}

// ReadVariableOps are inserted for each VarHandleOp to read the tensor values.

}

using LocalVarOp = std::variant<TF::VarHandleOp, TF::MlirLocalVarOp>;

Value LocalVarOp_resource(LocalVarOp &op) {
  if (auto var_handle_op = std::get_if<TF::VarHandleOp>(&op)) {
    return var_handle_op->getResource();
  } else {
    return std::get<TF::MlirLocalVarOp>(op).getResource();
  }
}

void LocalVarOp_erase(LocalVarOp &op) {
  if (auto var_handle_op = std::get_if<TF::VarHandleOp>(&op)) {
    var_handle_op->erase();

    }
  }
  return nullptr;
}

Type GetGlobalType(Operation* source) {
  if (auto var_handle_op = dyn_cast<TF::VarHandleOp>(source)) {
    // Resources are represented as tensor<resource<tensor<...>>>, so
    // unwrap until we get to the inner tensor<...>.
    auto tensor =
        llvm::dyn_cast<TensorType>(var_handle_op.getResource().getType());
    if (!tensor) return nullptr;
    TF::ResourceType resource =

    StringRef variable_name =
        GetNodeNameFromClassAttrOrSharedNameAttr(variable_v2_op);
    if (variable_name.empty()) {
      return signalPassFailure();
    }
    VarHandleOp var_handle_op = builder.create<VarHandleOp>(
        variable_v2_op.getLoc(),
        ArrayRef<Type>{RankedTensorType::get(
            {}, TF::ResourceType::get(ArrayRef<TensorType>{tensor_type},

// Test that the locs are properly set to their shared_names.
// CHECK-LOC: "tf.VarHandleOp"() <{{{.*shared_name = "var_0".*}}}>
// CHECK-LOC-SAME: loc("var_0")
// CHECK-LOC: "tf.VarHandleOp"() <{{{.*shared_name = "var_1".*}}}>
// CHECK-LOC-SAME: loc("var_1")
}

// -----

// RestoreV2 op not created for `AssignVariableOp(VarHandleOp, Const)` patterns
// in the initializer function of "init_op" type.

// a single string tensor as argument, which specifies the path to the
// checkpoint to which the variable's tensor values are saved. It finds
// `tf.AssignVariableOp(tf.VarHandleOp, tf.Const)` pattern in the initializer
// function of type "restore_op" to identify the VarHandleOps that should be
// saved using the SaveV2 op.
std::unique_ptr<OperationPass<ModuleOp>> CreateInsertSaveOpPass();

  attributes {tf_saved_model.exported_names = ["main"]} {
  // CHECK: tf.VarHandleOp
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable1"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>
  %1 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable2"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>

    %0 = "tf.VarHandleOp"() {_class = ["loc:@dense/kernel"], allowed_devices = [], container = "", device = "/job:worker/replica:0/task:1/device:CPU:0", shared_name = "var1"} : () -> tensor<!tf_type.resource<tensor<100x50xf32>>>
    %1 = "tf.ReadVariableOp"(%0) {device = ""} : (tensor<!tf_type.resource<tensor<100x50xf32>>>) -> tensor<100x50xf32>

    // CHECK: "tf.VarHandleOp"
    func.return
  }
}

// -----

// Test mutation detection propagates across function calls.

module {
  func.func @f() -> tensor<0xf32> {
    // CHECK: "tf.VarHandleOp"