/**
     * Creates a derived session using the given local repository.
     *
     * @param localRepository the new local repository
     * @return the derived session
     * @throws NullPointerException if {@code localRepository} is null
     */
    @Nonnull
    Session withLocalRepository(@Nonnull LocalRepository localRepository);

    /**
     * Creates a derived session using the given remote repositories.
     *

          scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT);
      auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a);
    } else {
      Output var =
          ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT);
      Output assign = ops::Assign(scope.WithOpName("assign"), var, a);
    }

    TF_ASSERT_OK(scope.ToGraphDef(&graph_def));

        error_code;
  }
  return queue_runner_def;
}

std::unique_ptr<Session> BuildSessionAndInitVariable(
    const GraphDef& graph_def) {
  SessionOptions options;
  std::unique_ptr<Session> session(NewSession(options));
  TF_CHECK_OK(session->Create(graph_def));

  TF_CHECK_OK(session->Run({}, {}, {kAssignOpName}, nullptr));
  return session;
}

TEST(QueueRunnerTest, BasicTest) {

		dsts[i] = typecheck.AssignExpr(v.Nname.(*ir.Name))
	}

	n.Results = ascompatee(n.Op(), dsts, n.Results)
	return n
}

// check assign type list to
// an expression list. called in
//
//	expr-list = func()
func ascompatet(nl ir.Nodes, nr *types.Type) []ir.Node {
	if len(nl) != nr.NumFields() {

        request.modelResolver(iSession.getData().get(SessionData.key(ModelResolver.class)));
        request.transformerContextBuilder(modelBuilder.newTransformerContextBuilder());
        request.systemProperties(session.getSystemProperties());
        request.userProperties(session.getUserProperties());
        request.modelCache(DefaultModelCache.newInstance(session, false));
        if (session.getCache() != null) {

// RUN: stablehlo-quant-opt %s -stablehlo-merge-fusion-with-dequantize -split-input-file -verify-diagnostics | FileCheck %s

// Merge fusion with dequantize for relu case.

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: func.func private @merge_relu_fusion
  func.func private @merge_relu_fusion(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {

		if !selectedUpdate {
			tx.Statement.Selects = append(tx.Statement.Selects, "*")
		}

		updateTx := tx.callbacks.Update().Execute(tx.Session(&Session{Initialized: true}))

		if updateTx.Error == nil && updateTx.RowsAffected == 0 && !updateTx.DryRun && !selectedUpdate {
			return tx.Session(&Session{SkipHooks: true}).Clauses(clause.OnConflict{UpdateAll: true}).Create(value)
		}

		return updateTx
	}

	return
}

  return BiasAddOp();
}

// Performs a fusion of the following pattern(s), if possible:
//   <Contraction> + BiasAdd + <Activation> -> <FusedContraction>
//
// Note that fusion with activation is preferred, but a contraction and BiasAdd
// can also be replaced by a _FusedConv2D if there is no other activation
// function.
// i.e., this class also supports the following fusion:
//   <Contraction> + BiasAdd -> <FusedContraction>
//

  // individual gradients.
  std::vector<Output> grad_outputs;
  TF_EXPECT_OK(AddSymbolicGradients(scope_test_, {y0, y2}, {x}, &grad_outputs));
  ClientSession session(scope_test_);
  std::vector<Tensor> grad_result;
  TF_EXPECT_OK(session.Run({{x, {3.0f}}}, grad_outputs, &grad_result));
  EXPECT_EQ(grad_result.size(), 1);
  EXPECT_EQ(grad_result[0].NumElements(), 1);
  EXPECT_EQ(grad_result[0].flat<float>()(0), 17502.0f);

	DB.Where(&User{Name: "find or create 3"}).Assign("age", 55).FirstOrCreate(&user4)

	if user4.Age != 55 {
		t.Errorf("Failed to set change to 55, got %v", user4.Age)
	}

	if updatedAt1.Format(time.RFC3339Nano) == user4.UpdatedAt.Format(time.RFC3339Nano) {
		t.Errorf("UpdateAt should be changed when update values with assign")
	}

	DB.Where(&User{Name: "find or create 4"}).Assign(User{Age: 44}).FirstOrCreate(&user4)