compile {
            outgoing {
                variants {
                    var1 {
                        artifact file('a1.jar')
                        attributes.attribute(buildType, 'profile')
                        attributes.attribute(flavor, 'bland')
                    }
                    var2 {
                        artifact file('a2.jar')

    %var_0 = "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
    "tf.AssignVariableOp"(%var_0, %cst_0) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()
    %add_0 = "tf.Identity"(%cst_0) : (tensor<2xf32>) -> tensor<2xf32>
    %var_1 = "tf.VarHandleOp"() {shared_name = "var_1"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>

    loop body inine):

    ```mlir
      %var0 = ...
      %var1 = ...
      tf.while (..., %var0, %var1) {
        tf_device.replicate ([%var0, %var1] as %rvar) {
          %compile:2 = "tf._TPUCompileMlir"()
          tf.TPUExecuteAndUpdateVariablesOp(%rvar, compile#1)
        }
      }
    ```

    This pass will transform it into

    ```mlir
      %var0 = ...
      %var1 = ...
      %state_var0 = ...

                if (!"value1".equals(System.getProperty("var1"))) {
                    throw new RuntimeException("Expected system property not specified (var1)");
                }
                if (!"value2".equals(System.getProperty("var2"))) {
                    throw new RuntimeException("Expected system property not specified (var2)");
                }
            }
        }
        '''

  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[VAR:.*]] = "tf.VarHandleOp"()
  // CHECK-SAME: "var1"
  // CHECK: %[[CST:.*]] = arith.constant dense<> : tensor<0xf32>
  // CHECK: "tf.AssignVariableOp"(%[[VAR]], %[[CST]])
}


// -----

// Test invalid tensor
// expected-error@below{{Can't find variable var5 in session}}
module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {

from tensorflow.compiler.mlir.tensorflow.tests.tf_saved_model import common_v1

# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR0:[a-zA-Z_0-9]+]]", type = tensor<5x3xf32>, value = {{.*}} : tensor<5x3xf32>}> : () -> ()
# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR1:[a-zA-Z_0-9]+]]", type = tensor<3x5xf32>, value = {{.*}} : tensor<3x5xf32>}> : () -> ()
# CHECK:      func {{@[a-zA-Z_0-9]+}}(

import static org.testng.Assert.*;

public class ParameterizedTest {

    @Test(dataProvider = "1")
	public void p1(String var1, String var2) {
        System.out.println("var1 is: " + var1);
        System.err.println("var2 is: " + var2);
       	assertEquals(var1, "1");
	}

	@DataProvider(name = "1")
	public Object[][] provider1() {
		return new Object[][] {
		   {"1", "2"},
		   {"3", "4"}

  // Verify that both variables are saved correctly.
  BundleReader bundle_reader(env_, *checkpoint_prefix);

  Tensor loaded_var_0{};
  EXPECT_TRUE(bundle_reader.Lookup("var_0", &loaded_var_0).ok());
  ExpectEqual(loaded_var_0, AsTensor<float>({1.0, 2.0}));

  Tensor loaded_var_1{};
  EXPECT_TRUE(bundle_reader.Lookup("var_1", &loaded_var_1).ok());

        compile {
            attributes.attribute(buildType, 'debug')
            outgoing {
                variants {
                    var1 {
                        artifact oneJar
                        attributes.attribute(flavor, 'one')
                    }
                    var2 {
                        artifact twoJar
                        attributes.attribute(flavor, 'two')
                    }
                }

  template <typename T>
  Var* CreateVariable(const string& name, const TensorShape& shape,
                      const gtl::ArraySlice<T> data) {
    Tensor* init_var_value = CreateDeviceTensor<T>(shape, data);
    Var* var = new Var(DataTypeToEnum<T>::v());
    *var->tensor() = *init_var_value;
    var->is_initialized = true;

    return var;
  }