[TF FE] Add complex type support for rank op (#23118)

### Details:
 - Add complex type support for rank op
 - Add corresponding test of it

### Tickets:
 - I think I don't have a ticket id

### Related issues

#22956

---------

Co-authored-by: Roman Kazantsev <[email protected]>

src/frontends/tensorflow_common/src/op/rank.cpp

@@ -3,8 +3,11 @@
 //
 
 #include "common_op_table.hpp"
+#include "helper_ops/complex_type_mark.hpp"
+#include "openvino/op/constant.hpp"
 #include "openvino/op/shape_of.hpp"
 #include "openvino/op/squeeze.hpp"
+#include "openvino/op/subtract.hpp"
 
 using namespace std;
 using namespace ov::op;
@@ -15,8 +18,21 @@ namespace tensorflow {
 namespace op {
 
 ov::OutputVector translate_rank_op(const NodeContext& node) {
-    default_op_checks(node, 1, {"Rank"});
+    default_op_checks(node, 1, {"Rank"}, true);
     auto input = node.get_input(0);
+    auto complex_type_mark = as_type_ptr<ComplexTypeMark>(input.get_node_shared_ptr());
+    if (complex_type_mark) {
+        input = complex_type_mark->input_value(0);
+        auto input_shape = make_shared<v3::ShapeOf>(input, ov::element::i32);
+
+        auto unsqueeze_input_rank = make_shared<v3::ShapeOf>(input_shape, ov::element::i32);
+        auto input_rank_with_complex = make_shared<v0::Squeeze>(unsqueeze_input_rank);
+        // eliminate the extra dimension
+        auto input_rank =
+            make_shared<v1::Subtract>(input_rank_with_complex, make_shared<v0::Constant>(ov::element::i32, Shape{}, 1));
+        set_node_name(node.get_name(), input_rank);
+        return {input_rank->output(0)};
+    }
     auto input_shape = make_shared<v3::ShapeOf>(input, ov::element::i32);
     auto unsqueeze_input_rank = make_shared<v3::ShapeOf>(input_shape, ov::element::i32);
     auto input_rank = make_shared<v0::Squeeze>(unsqueeze_input_rank);

tests/layer_tests/tensorflow_tests/test_tf_Rank.py

@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import pytest
+import numpy as np
 import tensorflow as tf
 from common.tf_layer_test_class import CommonTFLayerTest
 
@@ -33,3 +34,44 @@ def test_rank_basic(self, params, ie_device, precision, ir_version, temp_dir,
         self._test(*self.create_rank_net(**params),
                    ie_device, precision, ir_version, temp_dir=temp_dir,
                    use_legacy_frontend=use_legacy_frontend)
+
+class TestComplexRank(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        rng = np.random.default_rng()
+        assert 'param_real:0' in inputs_info
+        assert 'param_imag:0' in inputs_info
+        param_real_shape_1 = inputs_info['param_real:0']
+        param_imag_shape_1 = inputs_info['param_imag:0']
+        inputs_data = {}
+        inputs_data['param_real:0'] = 4 * rng.random(param_real_shape_1).astype(np.float32) - 2
+        inputs_data['param_imag:0'] = 4 * rng.random(param_imag_shape_1).astype(np.float32) - 2
+        return inputs_data
+
+    def create_rank_net(self, input_shape):
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            input_real = tf.compat.v1.placeholder(tf.float32, input_shape, 'param_real')
+            input_imag = tf.compat.v1.placeholder(tf.float32, input_shape, 'param_imag')
+            input = tf.raw_ops.Complex(real=input_real, imag=input_imag)
+            tf.raw_ops.Rank(input=input)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[]),
+        dict(input_shape=[1]),
+        dict(input_shape=[2, 6]),
+        dict(input_shape=[3, 4, 5, 6])
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_complex_rank(self, params, ie_device, precision, ir_version, temp_dir,
+                        use_legacy_frontend):
+        self._test(*self.create_rank_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_legacy_frontend=use_legacy_frontend)