[TF FE]: Support complex tensors for Sub operation

src/frontends/tensorflow/src/op_table.cpp

@@ -195,7 +195,6 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"Pow", CreatorFunction(translate_binary_op<v1::Power>)},
         {"RealDiv", CreatorFunction(translate_binary_op<v1::Divide>)},
         {"SquaredDifference", CreatorFunction(translate_binary_op<v0::SquaredDifference>)},
-        {"Sub", CreatorFunction(translate_binary_op<v1::Subtract>)},
 
         // note: ReduceOp translator declaration for each op must to be added in reduce.cpp file
         {"Any", CreatorFunction(translate_direct_reduce_op<v1::ReduceLogicalOr>)},
@@ -396,6 +395,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"StatelessIf", CreatorFunction(translate_if_op)},
         {"StatelessWhile", CreatorFunction(translate_while_op)},
         {"StridedSlice", CreatorFunction(translate_strided_slice_op)},
+        {"Sub", CreatorFunction(translate_sub_op)},
         {"Switch", CreatorFunction(translate_switch_op)},
         {"TensorArrayCloseV3", CreatorFunction(translate_tensor_array_close_v3_op)},
         {"TensorArrayConcatV3", CreatorFunction(translate_tensor_array_concat_v3_op)},

src/frontends/tensorflow_common/include/common_op_table.hpp

@@ -162,6 +162,7 @@ OP_CONVERTER(translate_split_v_op);
 OP_CONVERTER(translate_square_op);
 OP_CONVERTER(translate_squeeze_op);
 OP_CONVERTER(translate_strided_slice_op);
+OP_CONVERTER(translate_sub_op);
 OP_CONVERTER(translate_sqrt_op);
 OP_CONVERTER(translate_empty_tensor_list_op);
 OP_CONVERTER(translate_tensor_list_from_tensor_op);

src/frontends/tensorflow_common/src/op/binary_op.cpp

@@ -171,6 +171,33 @@ OutputVector translate_addv2_op(const NodeContext& node) {
     return {result};
 }
 
+OutputVector translate_sub_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"Sub"}, true);
+    auto lhs = node.get_input(0);
+    auto rhs = node.get_input(1);
+
+    auto complex_type_mark_lhs = as_type_ptr<ComplexTypeMark>(lhs.get_node_shared_ptr());
+    auto complex_type_mark_rhs = as_type_ptr<ComplexTypeMark>(rhs.get_node_shared_ptr());
+    auto complex_type_inputs = (complex_type_mark_lhs && complex_type_mark_rhs);
+
+    if (complex_type_inputs) {
+        lhs = complex_type_mark_lhs->input_value(0);
+        rhs = complex_type_mark_rhs->input_value(0);
+    }
+
+    // performing an actual operation
+    auto result = make_shared<v1::Subtract>(lhs, rhs);
+
+    if (complex_type_inputs) {
+        auto complex_result = make_shared<ComplexTypeMark>(result, complex_type_mark_lhs->get_complex_part_type());
+        set_node_name(node.get_name(), result);
+
+        return {complex_result};
+    }
+    set_node_name(node.get_name(), result);
+    return {result};
+}
+
 template OutputVector translate_binary_op<v1::Add>(const NodeContext& node);
 template OutputVector translate_binary_op<v13::BitwiseAnd>(const NodeContext& node);
 template OutputVector translate_binary_op<v13::BitwiseOr>(const NodeContext& node);

tests/layer_tests/tensorflow_tests/test_tf_Sub.py

@@ -216,3 +216,70 @@ def test_sub_placeholder_const_broadcast_5D(self, params, ie_device, precision,
                                                           use_legacy_frontend=use_legacy_frontend),
                    ie_device, precision, ir_version,
                    temp_dir=temp_dir, use_legacy_frontend=use_legacy_frontend)
+
+
+class TestComplexSub(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        rng = np.random.default_rng(84821)
+
+        assert 'param_real_x:0' in inputs_info
+        assert 'param_imag_x:0' in inputs_info
+
+        assert 'param_real_y:0' in inputs_info
+        assert 'param_imag_y:0' in inputs_info
+
+        param_real_shape_x = inputs_info['param_real_x:0']
+        param_imag_shape_x = inputs_info['param_imag_x:0']
+
+        param_real_shape_y = inputs_info['param_real_y:0']
+        param_imag_shape_y = inputs_info['param_imag_y:0']
+
+        inputs_data = {}
+        inputs_data['param_real_x:0'] =  rng.uniform(-10.0, 10.0, param_real_shape_x).astype(np.float32)
+        inputs_data['param_imag_x:0'] = rng.uniform(-10.0, 10.0, param_imag_shape_x).astype(np.float32)
+
+        inputs_data['param_real_y:0'] =  rng.uniform(-10.0, 10.0, param_real_shape_y).astype(np.float32)
+        inputs_data['param_imag_y:0'] = rng.uniform(-10.0, 10.0, param_imag_shape_y).astype(np.float32)
+
+        return inputs_data
+
+    def create_complex_sub_net(self, x_shape, y_shape, ir_version, use_legacy_frontend):
+        import tensorflow as tf
+
+        tf.compat.v1.reset_default_graph()
+        with tf.compat.v1.Session() as sess:
+            param_real_x = tf.compat.v1.placeholder(np.float32, x_shape, 'param_real_x')
+            param_imag_x = tf.compat.v1.placeholder(np.float32, x_shape, 'param_imag_x')
+
+            param_real_y = tf.compat.v1.placeholder(np.float32, y_shape, 'param_real_y')
+            param_imag_y = tf.compat.v1.placeholder(np.float32, y_shape, 'param_imag_y')
+
+            x = tf.raw_ops.Complex(real=param_real_x, imag=param_imag_x)
+            y = tf.raw_ops.Complex(real=param_real_y, imag=param_imag_y)
+
+            result = tf.subtract(x, y, name="Operation")
+
+            tf.raw_ops.Real(input=result)
+            tf.raw_ops.Imag(input=result)
+
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        ref_net = None
+
+        return tf_net, ref_net
+
+    @pytest.mark.parametrize('x_shape, y_shape', [
+        [[5, 5], [5]],
+        [[4, 10], [4, 1]],
+        [[1, 3, 50, 224], [1]],
+        [[10, 10, 10], [10, 10, 10]],
+    ])
+    @pytest.mark.precommit
+    @pytest.mark.nightly
+    def test_complex_sub(self, x_shape, y_shape, 
+                   ie_device, precision, ir_version, temp_dir, use_legacy_frontend):
+        self._test(*self.create_complex_sub_net(x_shape, y_shape, ir_version=ir_version,
+                                          use_legacy_frontend=use_legacy_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_legacy_frontend=use_legacy_frontend)