[TF FE] Support complex tensors for Reciprocal operations

src/frontends/tensorflow_common/src/op/reciprocal.cpp

@@ -3,6 +3,7 @@
 //
 
 #include "common_op_table.hpp"
+#include "helper_ops/complex_type_mark.hpp"
 #include "openvino/op/power.hpp"
 #include "utils.hpp"
 
@@ -16,13 +17,27 @@ namespace op {
 
 OutputVector translate_reciprocal_op(const NodeContext& node) {
     // computes element-wise 1/x, where x - input
-    default_op_checks(node, 1, {"Reciprocal"});
+    default_op_checks(node, 1, {"Reciprocal"}, true);
     auto x = node.get_input(0);
     auto minus_one_const = create_same_type_const_scalar<int32_t>(x, -1);
-    auto reciprocal = make_shared<v1::Power>(x, minus_one_const);
+
+    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 reciprocal = make_shared<v1::Power>(complex_type_mark_x, minus_one_const);
+
     set_node_name(node.get_name(), reciprocal);
+    if (complex_type_mark_x) {
+        auto complex_reciprocal = make_shared<ComplexTypeMark>(reciprocal, complex_type_mark_x);
+        return {complex_reciprocal->output(0)};
+    }
+
     return {reciprocal};
 }
+
 }  // namespace op
 }  // namespace tensorflow
 }  // namespace frontend

tests/layer_tests/tensorflow_tests/test_tf_Reciprocal.py

@@ -41,3 +41,50 @@ def test_reciprocal_basic(self, params, ie_device, precision, ir_version, temp_d
         self._test(*self.create_reciprocal_net(**params),
                    ie_device, precision, ir_version, temp_dir=temp_dir,
                    use_legacy_frontend=use_legacy_frontend)
+
+class TestComplexReciprocal(CommonTFLayerTest):
+        def _prepare_input(self, inputs_info):
+            rng = np.random.default_rng()
+            assert 'param_real_1:0' in inputs_info
+            assert 'param_imag_1:0' in inputs_info
+            param_real_shape_1 = inputs_info['param_real_1:0']
+            param_imag_shape_1 = inputs_info['param_imag_1:0']
+            inputs_data = {}
+            inputs_data['param_real_1:0'] = 4 * rng.random(param_real_shape_1).astype(np.float32) - 2
+            inputs_data['param_imag_1:0'] = 4 * rng.random(param_imag_shape_1).astype(np.float32) - 2
+            return inputs_data
+
+        def create_complex_reciprocal_net(self, input_shape):
+            import tensorflow as tf
+            tf.compat.v1.reset_default_graph()
+            # Create the graph and model
+            with tf.compat.v1.Session() as sess:
+                param_real1 = tf.compat.v1.placeholder(np.float32, input_shape, 'param_real_1')
+                param_imag1 = tf.compat.v1.placeholder(np.float32, input_shape, 'param_imag_1')
+                complex_x = tf.raw_ops.Complex(real=param_real1, imag=param_imag1)
+                reciprocal = tf.raw_ops.Reciprocal(x=complex_x)
+                real = tf.raw_ops.Real(input=reciprocal)
+                img = tf.raw_ops.Imag(input=reciprocal)
+                tf.compat.v1.global_variables_initializer()
+                tf_net = sess.graph_def
+
+            return tf_net, None
+
+
+        test_data_basic = [
+            dict(input_shape=[]),
+            dict(input_shape=[2]),
+            dict(input_shape=[1, 3]),
+            dict(input_shape=[2, 3, 4]),
+            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_reciprocal(self, params, ie_device, precision, ir_version, temp_dir,
+                            use_legacy_frontend):
+            self._test(
+                *self.create_complex_reciprocal_net(**params),
+                ie_device, precision, ir_version, temp_dir=temp_dir,
+                use_legacy_frontend=use_legacy_frontend)