Support complex tensors for Pack operation

src/frontends/tensorflow_common/src/op/pack.cpp

@@ -6,6 +6,7 @@
 #include "openvino/op/concat.hpp"
 #include "openvino/op/constant.hpp"
 #include "openvino/op/unsqueeze.hpp"
+#include "helper_ops/complex_type_mark.hpp"
 
 using namespace std;
 using namespace ov::op;
@@ -16,20 +17,31 @@ namespace tensorflow {
 namespace op {
 
 OutputVector translate_pack_op(const NodeContext& node) {
-    default_op_checks(node, 1, {"Pack", "PACK"});
-    auto num_size = static_cast<int>(node.get_input_size());
+    default_op_checks(node, 1, {"Pack", "PACK"}, true);
 
     auto axis = node.get_attribute<int64_t>("axis", 0);
+
+    auto num_size = static_cast<int>(node.get_input_size());
+    auto complex_type_mark = as_type_ptr<ComplexTypeMark>(node.get_input(0).get_node_shared_ptr());
     auto axis_const = make_shared<v0::Constant>(element::i64, Shape{}, axis);
 
+    if (complex_type_mark) {
+        axis_const = make_shared<v0::Constant>(element::i64, Shape{}, axis + 1);
+    } 
+
     OutputVector concat_inputs;
     for (int ind = 0; ind < num_size; ++ind) {
         auto in = node.get_input(ind);
         concat_inputs.push_back(make_shared<v0::Unsqueeze>(in, axis_const));
     }
 
     auto pack = make_shared<v0::Concat>(concat_inputs, axis);
+
     set_node_name(node.get_name(), pack);
+    if (complex_type_mark) {
+        auto complex_result = make_shared<ComplexTypeMark>(pack, complex_type_mark->get_complex_part_type());
+        return {complex_result};
+    }
     return {pack};
 }
 }  // namespace op

tests/layer_tests/tensorflow_tests/test_tf_Pack.py

@@ -51,3 +51,55 @@ def test_pack_basic(self, params, ie_device, precision, ir_version, temp_dir,
         self._test(*self.create_pack_net(**params),
                    ie_device, precision, ir_version, temp_dir=temp_dir,
                    use_legacy_frontend=use_legacy_frontend)
+
+
+class TestComplexPack(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        inputs_data = {}
+        for input_name, input_shape in inputs_info.items():
+            inputs_data[input_name] = np.random.randint(-5, 5, input_shape).astype(self.input_type)
+        return inputs_data
+
+    def create_pack_net(self, input_shape, input_num, axis, input_type):
+        self.input_type = input_type
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            inputs = []
+            type_map = {
+                np.float32: tf.float32,
+                np.int32: tf.int32,
+            }
+            assert input_type in type_map, "Test error: need to update type_map"
+            tf_type = type_map[input_type]
+            complex_inputs = []
+            for ind in range(input_num):
+                input_real = tf.compat.v1.placeholder(tf_type, input_shape, 'input' + str(ind) + '_real')
+                input_imag = tf.compat.v1.placeholder(tf_type, input_shape, 'input' + str(ind) + '_imag')
+                inputs.append(input_real)
+                inputs.append(input_imag)
+
+                complex_inputs.append(tf.raw_ops.Complex(real=input_real, imag=input_imag))
+            if axis is not None:
+                tf.raw_ops.Pack(values=complex_inputs, axis=axis)
+            else:
+                tf.raw_ops.Pack(values=complex_inputs)
+            tf.compat.v1.global_variables_initializer()
+
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[2, 4], input_num=2, axis=None, input_type=np.float32),
+        dict(input_shape=[3, 1, 2], input_num=3, axis=1, input_type=np.int32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_pack_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                        use_legacy_frontend):
+        self._test(*self.create_pack_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_legacy_frontend=use_legacy_frontend)