add test cases

tests/integration/frameworks/models/tensorflow_v2.py

@@ -54,6 +54,30 @@ def __call__(self, x1: tf.Tensor, x2: tf.Tensor, factor: tf.Tensor):
         return self.dense(x1 + x2 * factor)
 
 
+class MultiOutputModel(tf.Module):
+    def __init__(self):
+        super().__init__()
+        self.v = tf.Variable(2.0)
+
+    @tf.function(input_signature=[tf.TensorSpec([1, 5], tf.float32)])
+    def __call__(self, x: tf.Tensor):
+        return (x * self.v, x)
+
+
+# This model could have 2 output signatures depends on the input
+class MultiOutputModel2(tf.Module):
+    def __init__(self):
+        super().__init__()
+        self.v = tf.Variable(2.0)
+
+    @tf.function
+    def __call__(self, x):
+        if x.shape[0] > 2:
+            return (x * self.v, x)
+        else:
+            return x
+
+
 def make_keras_sequential_model() -> tf.keras.models.Model:
     net = keras.models.Sequential(
         (
@@ -155,6 +179,76 @@ def make_keras_functional_model() -> tf.keras.Model:
     ],
 )
 
+native_multi_output_model = FrameworkTestModel(
+    name="tf2",
+    model=MultiOutputModel(),
+    configurations=[
+        Config(
+            test_inputs={
+                "__call__": [
+                    Input(
+                        input_args=[i],
+                        expected=lambda out: np.isclose(out[0], input_array * 2).all(),
+                    )
+                    for i in [
+                        input_tensor,
+                        input_tensor_f32,
+                        input_array,
+                        input_array_i32,
+                        input_data,
+                    ]
+                ],
+            },
+        ),
+    ],
+)
+
+input_array2 = np.arange(15, dtype=np.float32).reshape((3, 5))
+input_array2_i32 = np.array(input_array2, dtype="int64")
+input_tensor2 = tf.constant(input_array2, dtype=tf.float64)
+input_tensor2_f32 = tf.constant(input_array2, dtype=tf.float32)
+
+multi_output_model2 = MultiOutputModel2()
+# feed some data for tracing
+_ = multi_output_model2(np.array(input_array, dtype=np.float32))
+_ = multi_output_model2(input_array2)
+
+native_multi_output_model2 = FrameworkTestModel(
+    name="tf2",
+    model=multi_output_model2,
+    configurations=[
+        Config(
+            test_inputs={
+                "__call__": [
+                    Input(
+                        input_args=[i],
+                        expected=lambda out: np.isclose(out, i).all(),
+                    )
+                    for i in [
+                        input_tensor,
+                        input_tensor_f32,
+                        input_array,
+                        input_array_i32,
+                        input_data,
+                    ]
+                ]
+                + [
+                    Input(
+                        input_args=[i],
+                        expected=lambda out: np.isclose(out[0], input_array2 * 2).all(),
+                    )
+                    for i in [
+                        input_tensor2,
+                        input_tensor2_f32,
+                        input_array2,
+                        input_array2_i32,
+                    ]
+                ],
+            },
+        ),
+    ],
+)
+
 keras_models = [
     FrameworkTestModel(
         name="tf2",
@@ -186,7 +280,10 @@ def make_keras_functional_model() -> tf.keras.Model:
         make_keras_sequential_model(),
     ]
 ]
+
 models: list[FrameworkTestModel] = keras_models + [
     native_model,
     native_multi_input_model,
+    native_multi_output_model,
+    native_multi_output_model2,
 ]