[TF FE] Support complex tensors for OnesLike operation (openvinotoolk…

…it#23445)

### Details:
 - *Adding support for OnesLike operation on complex type tensor*
- Closes openvinotoolkit#22953

---------

Co-authored-by: Michal Lukaszewski <michal.lukaszewski@intel.com>
Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>

src/frontends/tensorflow_common/src/op/ones_like.cpp

@@ -3,9 +3,11 @@
 //
 
 #include "common_op_table.hpp"
+#include "helper_ops/complex_type_mark.hpp"
 #include "openvino/op/broadcast.hpp"
 #include "openvino/op/concat.hpp"
 #include "openvino/op/constant.hpp"
+#include "openvino/op/gather.hpp"
 #include "openvino/op/shape_of.hpp"
 #include "openvino/op/squeeze.hpp"
 #include "utils.hpp"
@@ -19,8 +21,28 @@ namespace tensorflow {
 namespace op {
 
 OutputVector translate_ones_like_op(const NodeContext& node) {
-    default_op_checks(node, 1, {"OnesLike"});
+    default_op_checks(node, 1, {"OnesLike"}, true);
     auto x = node.get_input(0);
+    auto complex_type_mark_x = as_type_ptr<ComplexTypeMark>(x.get_node_shared_ptr());
+    if (complex_type_mark_x) {
+        x = complex_type_mark_x->input_value(0);
+        auto gather_index_real = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
+        auto minus_one = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
+        auto x_real = make_shared<v8::Gather>(x, gather_index_real, minus_one)->output(0);
+        Output<Node> shape_of_real = make_shared<v3::ShapeOf>(x_real, element::i32);
+
+        auto one_const = create_same_type_const_scalar<int32_t>(x_real, 1);
+        Output<Node> ones_like = make_shared<v3::Broadcast>(one_const, shape_of_real);
+
+        auto zero_const = create_same_type_const_scalar<int32_t>(x_real, 0);
+        Output<Node> zeros_like = make_shared<v3::Broadcast>(zero_const, shape_of_real);
+        auto result = make_shared<v0::Concat>(OutputVector{ones_like, zeros_like}, -1);
+        set_node_name(node.get_name(), result);
+        auto ones_like_complex = make_shared<ComplexTypeMark>(result, complex_type_mark_x->get_complex_part_type());
+
+        return {ones_like_complex};
+    }
+
     Output<Node> shape_of = make_shared<v3::ShapeOf>(x, element::i32);
     auto one_const = create_same_type_const_scalar<int32_t>(x, 1);
 
@@ -35,11 +57,9 @@ OutputVector translate_ones_like_op(const NodeContext& node) {
     // remove extra dimension by squeezing
     auto zero_dim_ind = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
     ones_like = make_shared<v0::Squeeze>(ones_like, zero_dim_ind);
-
     set_node_name(node.get_name(), ones_like.get_node_shared_ptr());
     return {ones_like};
 }
-
 }  // namespace op
 }  // namespace tensorflow
 }  // namespace frontend

tests/layer_tests/tensorflow_tests/test_tf_OnesLike.py

@@ -43,3 +43,48 @@ def test_ones_like(self, params, ie_device, precision, ir_version, temp_dir,
         self._test(*self.create_ones_like_net(**params),
                    ie_device, precision, ir_version, temp_dir=temp_dir,
                    use_legacy_frontend=use_legacy_frontend)
+
+
+class TestComplexOnesLike(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        rng = np.random.default_rng()
+        assert 'x_real:0' in inputs_info
+        assert 'x_imag:0' in inputs_info
+        x_real_shape = inputs_info['x_real:0']
+        x_imag_shape = inputs_info['x_imag:0']
+        inputs_data = {}
+        inputs_data['x_real:0'] = 4 * rng.random(x_real_shape).astype(self.x_type) - 2
+        inputs_data['x_imag:0'] = 4 * rng.random(x_imag_shape).astype(self.x_type) - 2
+        return inputs_data
+
+    def create_complex_ones_like_net(self, x_shape, x_type):
+        self.x_type = x_type
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            x_real = tf.compat.v1.placeholder(tf.dtypes.as_dtype(x_type), x_shape, 'x_real')
+            x_imag = tf.compat.v1.placeholder(tf.dtypes.as_dtype(x_type), x_shape, 'x_imag')
+            x_complex = tf.raw_ops.Complex(real=x_real, imag=x_imag)
+            ones_like = tf.raw_ops.OnesLike(x=x_complex)
+            real = tf.raw_ops.Real(input=ones_like)
+            img = tf.raw_ops.Imag(input=ones_like)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(x_shape=[], x_type=np.float32),
+        dict(x_shape=[2], x_type=np.float32),
+        dict(x_shape=[2, 3, 4], x_type=np.float32),
+        dict(x_shape=[1, 4, 3, 1], x_type=np.float32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_complex_ones_like(self, params, ie_device, precision, ir_version, temp_dir,
+                       use_legacy_frontend):
+        self._test(*self.create_complex_ones_like_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_legacy_frontend=use_legacy_frontend)