%0 = "tfl.var_handle"() : () -> tensor<!tf_type.resource>
  %1 = "tfl.dequantize"(%arg0) : (tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>) -> tensor<1x2x1x3xf32>
  "tfl.assign_variable"(%0, %1) : (tensor<!tf_type.resource>, tensor<1x2x1x3xf32>) -> ()
  func.return %arg0 : tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>

// CHECK-DAG: %[[CST_0:.*]] = "tf.Const"() {{.*value = dense<1.000000e\+00> : tensor<2xf32>.*}}
// CHECK-DAG: %[[VAR_HANDLE:.*]] = "tf.VarHandleOp"() {{.*shared_name = "var_0".*}} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
// CHECK: "tf.AssignVariableOp"(%[[VAR_HANDLE]], %[[CST_0]]) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()

  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* var_handle = TestVariable(ctx, 2.0, dev2_name);
  EXPECT_NE(var_handle, nullptr);
  TFE_ContextAsyncWait(ctx, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  const string function_def = VariableAddFunction();

  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_TensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TFE_Execute(op, &var_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_DeleteOp(op);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  CHECK_EQ(1, num_retvals);
  TF_DeleteStatus(status);
  return var_handle;
}

        %add = "tf.AddV2"(%cst, %cst) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
        %var_handle = "tf.VarHandleOp"() {container = "", device = "/device:CPU:0", shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
        "tf.AssignVariableOp"(%var_handle, %add) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()
        return
      }
    }
  )mlir";

}

Node* MakeWrite(const Scope& scope, Output value_to_write, const string& id) {
  Output var_handle = ops::VarHandleOp(scope.WithOpName("Var_" + id), DT_FLOAT,
                                       TensorShape({}));
  ops::AssignVariableOp assign_op(scope.WithOpName("Assignee_" + id),
                                  var_handle, value_to_write);
  return assign_op.operation.node();
}

  WalkResult walk_res = func_op.walk<WalkOrder::PreOrder>([&](Operation* op) {
    if (auto var_handle = dyn_cast<VarHandleOp>(op)) {
      // Record VarHandleOp's device attribute.
      StringRef device_attr = GetDeviceAttr(op);
      if (device_attr.empty()) return WalkResult::advance();
      auto res = AddResourceDeviceAndEmitError(var_handle.getResource(),
                                               device_attr, op, result);

  // created with `isolate_session_state` set to false, and update should
  // preserve it.
  {
    // Create a handle to `var`, using `ctx_1`.
    TFE_TensorHandle* var_handle =
        CreateVarHandle(ctx_1, remote_device, /*variable_name=*/"var");

    TFE_TensorHandle* handle_1 = nullptr;
    int num_retvals = 1;
    TF_Status* status = TF_NewStatus();

      %read1 = "tf.ReadVariableOp"(%arg1) : (!tf_res) -> tensor<32xf32>
      // expected-remark@below {{Result #0, ID 0 : 0}}
      %var_handle = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> !tf_res
      %read2 = "tf.ReadVariableOp"(%var_handle) : (!tf_res) -> tensor<32xf32>
      "tf.AssignVariableOp"(%arg1, %read0) : (!tf_res, tensor<32xf32>) -> ()

      %read1 = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
      %var_handle = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      %read2 = "tf.ReadVariableOp"(%var_handle) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>