// Verify tf.aliasing_output attributes on args are dropped during export.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 121 : i32}} {
  func.func @main(%arg0: tensor<*xf32> {tf.aliasing_output = 0 : i64}, %arg1: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<2x4x6x8xi32> {tf.device = "/CPU:1"})
  attributes  {tf.entry_function = {inputs = "args_0,args_1", outputs = "rets_0,rets_1"}} {

// Start step 0
C_0 = cond(args_0)
N_0 = non_tpu(args_0)
if (C_0) {
   F_0 = forward(args_0, N_0)
   T_0 = core_tpu(args_0, N_0, F_0)
   // B_0 = backward() is not evaluated here.
}

args_1 = update_args(args_0, N_0, T_0)

// Start step 1
C_1 = cond(args_1)
N_1 = non_tpu(args_1)
if (C_1) {
   F_1 = forward(args_1, N_1)
   // T_1 = core_tpu() is not evaluated here.

    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "spc1"
  op: "StatefulPartitionedCall"
  input: "input0"
  input: "args_0"
  attr {
    key: "Tin"
    value {
      list {

    func.return %4, %6 : tensor<!tf_type.variant>, tensor<!tf_type.variant>
  }
  // CHECK-LABEL: @funcB_renamed
  func.func private @funcB_renamed(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "funcB", tf._tf_data_function = true, tf.signature.is_stateful} {
    %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i64>} : () -> tensor<i64>

    func.return %4 : tensor<!tf_type.variant>
  }
  // CHECK-LABEL: __inference_Dataset_flat_map_lambda_190
  func.func private @__inference_Dataset_flat_map_lambda_190(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "__inference_Dataset_flat_map_lambda_19", tf._tf_data_function = true, tf.signature.is_stateful} {

    key: "parallel_copy"
    value {
      b: false
    }
  }
}
library {
  function {
    signature {
      name: "__inference_Dataset_map_<lambda>_8"
      input_arg {
        name: "args_0"
        type: DT_INT32
      }
      output_arg {
        name: "identity"
        type: DT_INT32
      }
    }
    node_def {
      name: "mul/y"
      op: "Const"
      attr {

  l.append(Integer(3));
  EXPECT_EQ(l.Get<Integer>(0)->get(), 3);
  EXPECT_EQ(l.size(), 1);
}

TaggedValue AddIntegers(TaggedValue args_, TaggedValue kwargs_) {
  auto& args = args_.tuple();
  // auto& kwargs = kwargs_.dict();
  return TaggedValue(args[0].i64() + args[1].i64());
}

TEST(ObjectTest, TestCast) {
  Integer i(3);
  auto result = Cast<String>(i);
  ASSERT_TRUE(!result.ok());
}

                         AbstractContext* ctx) {
  auto CallFn = [fn_name, fn, ctx](TaggedValue args_,
                                   TaggedValue kwargs_) -> TaggedValue {
    std::cout << "Calling " << fn_name << std::endl;
    tensorflow::StatusOr<TaggedValue> v = fn.Execute(ctx, args_);
    return v.value();
  };
  return TaggedValue(CallFn);
}

// Import a module from a saved model.
//

Daniel Martí <******@****.***> 1610820211 +0000

        assertThat(args2.length, is(1));

        final ParameterizedClassDesc arg2_1 = args2[0];
        assertThat(arg2_1.getRawClass(), is(sameClass(Integer.class)));
    }

    /**
     * @throws Exception
     */
    @Test
    public void testMethodParameterType() throws Exception {